This dissertation includes two parallel topics: optical modeling of wavelength converters based on semiconductor optical amplifiers (SOA) and optical modeling for LEDs and solid state lighting. A steady-state numerical model of wavelength converters based on cross-gain SOAs is developed. In this model, a new model of the gain coefficient is applied. Each physical variable, such as the carrier density, gain coefficient, differential gain, and internal loss, spatially varies across the SOA cavity and is numerically calculated throughout the device. Increased accuracy over previous studies is achieved by including such spatial variations. This model predicts wavelength-dependent characteristics of a wavelength converter of the SOA in both large and small signal regimes. Some key performance factors of SOA wavelength converters. A hybrid method incorporating both guided wave optics and optical ray tracing is also developed to model LEDs and solid state lighting. This method can model either single wavelength or dual-wavelength LED structures with different die shapes and packages. The waveguide and diffraction optics are mainly used to model the near-field optics inside LED chips and its vicinity and to identify guided modes and leakage modes. Geometrical ray tracing is applied to model the far-field pattern and light interactions at different material interfaces, such as LED chip structures, LED package materials, and light scattering at those rough surfaces and textures. To improve LED light extraction efficiency, different LED die shapes and device structures can also be optimized using this method. New technologies for future research on SOAs and LEDs are also proposed.

The master’s thesis expresses an opinion on trends in UAV integration into non-segregated airspace issue. In the beginning, barriers to integration are characterized and a broader context is shown. Since necessity of the technical realization of the “see and be seen” principle exists, requirements for so called Sense and Avoid systems are presented. Various methods of Sense and Avoid are briefly described, highlighting their contribution to air safety and their potential for future development. The UAV communication topic is described in the last chapter, providing a list of the volume of transmitted messages, analyzing data link frequencies and selecting appropriate means of UAV operations.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an automated grid-based alignment system utilizing lasers and an array of light-detecting photodiodes. The intent is to create an inexpensive and scalable alignment system for pick-and-place robotic systems. The system utilizes the transformation matrix, geometry, and trigonometry to determine the movements to align the robot with a grid-based array of photodiodes. The alignment system consists of a sending unit utilizing lasers, a receiving module consisting of photodiodes, a data acquisition unit, a computer-based control system, and the robot being aligned. The control system computes the robot movements needed to position the lasers based on the laser positions detected by the photodiodes. A transformation matrix converts movements from the coordinate system of the grid formed by the photodiodes to the coordinate system of the robot. The photodiode grid can detect a single laser spot and move it to any part of the grid, or it can detect up to four laser spots and use their relative positions to determine rotational misalignment of the robot. Testing the alignment consists of detecting the position of a single laser at individual points in a distinct pattern on the grid array of photodiodes, and running the entire alignment process multiple times starting with different misalignment cases. The first test provides a measure of the position detection accuracy of the system, while the second test demonstrates the alignment accuracy and repeatability of the system. The system detects the position of a single laser or multiple lasers by using a method similar to a center-of-gravity calculation. The intensity of each photodiode is multiplied by the X-position of that photodiode. The summed result from each photodiode intensity and position product is divided by the summed value of all of the photodiode intensities to get the X-position of the laser. The same thing is done with the Y-values to get the Y-position of the laser. Results show that with this method the system can read a single laser position value with a resolution of 0.1mm, and with a maximum X-error of 2.9mm and Y-error of 2.0mm. It takes approximately 1.5 seconds to process the reading. The alignment procedure calculates the initial misalignment between the robot and the grid of photodiodes by moving the robot to two distinct points along the robot’s X-axis so that only one laser is over the grid. Using these two detected points, a movement trajectory is generated to move that laser to the X = 0, Y = 0 position on the grid. In the process, this moves the other three lasers over the grid, allowing the system to detect the positions of four lasers and uses the positions to determine the rotational and translational offset needed to align the lasers to the grid of photodiodes. This step is run in a feedback loop to update the adjustment until it is within a permissible error value. The desired result for the complete alignment is a robot manipulator positioning within ±0.5mm along the X and Y-axes. The system shows a maximum error of 0.2mm in the X-direction and 0.5mm in the Y-direction with a run-time of approximately 4 to 5 minutes per alignment. If the permissible error value of the final alignment is tripled the alignment time goes down to 1 to 1.5 minutes and the maximum error goes up to 1.4mm in both the X and Y-directions. The run time of the alignment decreases because the system runs fewer alignment iterations.