The goal of this work is to develop a portable and accurate colorblind test that has advantages over the HRR and Ishihara plate tests, including that it is easier and faster to perform, does not require the subject to identify alphanumeric characters or geometric shapes, provides unambiguous results to the provider without interpretation, and is at least 8 times faster. The advantage over prior anomaloscopes is that it can be made in a hand-held version, uses binary matching choices rather than having the subject match colors with a tuning knob, and uses optimal reference color choices determined from established knowledge of human color perception. To successfully achieve this, cone spectral sensitivity curves and all subsets of four LEDs from a set of eight spanning the visible spectrum, the 1x4 metamer solution for a reference color for normal vision, deuteranomaly, and protanomaly are calculated. From these solutions, the optimized set of 4 LEDS was determined by maximizing the average angle between the normal, deuteranomaly, and protanomaly metamer solution vectors in the XYZ color space. To perform the test, the subject is asked to determine the best match for color and brightness in side-by-side display panels illuminated with distinctly different reference metamer color pairs for normal, deuteranomaly, and protanomaly vision. This allows the operator to directly and unambiguously determine the subject’s color vision type. The average duration to perform the tests are 30, 253, and 281 seconds for the anomaloscope, Ishihara 38 plate test, and HRR 24 plate test, respectively. When determining whether the subject has normal vision or is colorblind, the anomaloscope and HRR test results agreed for all 102 subjects. Because this rendition of the anomaloscope was designed to only distinguish between normal, deuteranomalous, and protanomalous vision, the 7 subjects that the HRR determined to be tritanomalous were not included in the results presented hereafter. The HRR 24 plate test and the anomaloscope agreed in their diagnosis 91/95 = 96% of the time, the Ishihara 38 plate test and the anomaloscope agreed in their diagnosis 94/101 = 93% of the time, and the HRR and the Ishihara agreed in their diagnosis 89/95 = 94% of the time. The approach described here can be extended to other types of color blindness.

Visual odometry (VO) plays a crucial role in determining the position and orientation of an autonomous vehicle as it navigates through its environment. However, the performance of visual odometry can be significantly affected by errors in disparity estimation and LIDAR depth measurements. This thesis investigates the use of LIDAR depth correction and Stereo disparity matching, combined with stronger match filtering, to improve the accuracy and reliability of VO estimations. The study utilizes a dataset consisting of a sequence of image frames, ground truth position data, and a range of feature detection, description, and matching techniques. Results indicate that the proposed approach significantly improves the accuracy of VO estimations, providing a valuable contribution to the development of reliable and safe autonomous navigation systems. The proposed method consists of two main components: (1) an advanced disparity matching algorithm to obtain more accurate and robust disparity estimations, and (2) a LIDAR depth correction module that employs a sensor fusion approach to refine the depth information generated by LIDAR sensors. The LIDAR depth correction module combines data from multiple sensors, including LIDAR, camera, and inertial measurement unit (IMU), to produce a more accurate depth estimation. The performance of the proposed approach is evaluated using real-world datasets and benchmark visual odometry challenges. Results demonstrate that the proposed method significantly improves the accuracy and robustness of visual odometry, leading to better localization and navigation performance for autonomous vehicles. This research contributes to the ongoing development of autonomous vehicle technology by addressing critical challenges in visual odometry and offering a practical solution for more accurate and reliable self-localization

As online media, including social media platforms, become the primary and go-to resource for traditional communication, news and the spread of information is more present and accessible to consumers than ever before. This research focuses on analyzing Twitter data on the ongoing Russian-Ukrainian War to understand the significance of social media during this period in comparison to previous conflicts. The significance of social media and political conflict will be examined through Twitter user analysis and sentiment analysis. This case study will conduct sentiment analysis on a random sample of tweets from a given dataset, followed by user analysis and classification methods. The data will explore the implications for understanding public opinion on the conflict, the strengths and limitations of Twitter as a data source, and the next steps for future research. Highlighting the implications of the research findings will allow consumers and political stakeholders to make more informed decisions in the future.