Doctor of Philosophy (PhD), Ohio University, 2025, Electrical Engineering & Computer Science (Engineering and Technology)

The human genome is the blueprint of making a human. It contains all the genetic information needed to build an entire organism and is encoded in the DNA sequence. There are genes in DNA, which provide information for making proteins to perform the functions of the cells. Understanding the genome is essential for learning biological mechanisms, cell developments, and diseases. The 3D genome refers to the three-dimensional structure of the genome within the nucleus. With technologies such as Hi-C and Genome Architecture Mapping, scientists can study the relationship between chromatin interactions and gene regulation. However, there are limited bioinformatics tools that analyze the biological elements in chromatin contacts. In this dissertation, a bioinformatics pipeline that analyzes the feature pairs in chromatin contacts were developed and this pipeline was applied to three case studies. In the first case study pyramidal glutamatergic neurons and dopaminergic neurons were compared. The transcription factor binding sites on the open chromatin region of each cell type were determined, the feature pair analysis pipeline was applied to each cell type, and the transcription factor binding site pairs that have the highest discriminatory power between these two cell types were observed. The results show that the chromatin contacts with these strong discriminatory power transcription factor pairs contain more expressed genes, which are related to cell functions. The second case study compared two techniques that capture three-dimensional chromatin interactions: Genome Architecture Mapping (GAM) and Hi-C. The feature pair analysis pipeline used ChIP-seq data as features. It shows that GAM can capture more active chromatin interactions than Hi-C, while Hi-C captures more inactive chromatin interactions than GAM. The third case study explored early cell development, which analyzed the feature pairs between Embryonic Stem Cells (ESCs) and Extra-embryonic Endoderm (open full item for complete abstract)

Committee: Lonnie Welch (Advisor); David Juedes (Committee Member); Kevin Lee (Committee Member); Chang Liu (Committee Member); Chunmin Lo (Committee Member); Jundong Liu (Committee Member) Subjects: Computer Science

Master of Science, The Ohio State University, 2021, Mechanical Engineering

Bipedal robots are becoming more popular in performing tasks in an environment that is designed for humans. For this purpose, most bipedal robots are equipped with various sensors to sense the robot's environment. From the measurements of the sensors, a perception system is implemented to translate and convert the raw data into a meaningful format corresponding to the tasks and also provide safety for humans, properties in the environment as well as the robot itself. This thesis presents the implementation of a perception system using various sensors available to a bipedal robot, Digit, to obtain objectively useful information of the environment as well as the state of the robot itself. Various methods of data processing were applied to available sensor measurements, then a mapping algorithm was implemented to generate a 3D model of the environment. Simultaneous localization and mapping (SLAM) algorithm was also implemented to perform mapping and provide odometry for localization in the absence of an external source of odometry. We found that performing SLAM using Light Detection and Ranging sensor (LiDAR) performs exceptionally well on the bipedal robot in closed indoor space. Additionally, state estimation is implemented with Invariant Extended Kalman filter using inertial measurement data and the assumption of contact points to predict the state of the robot over time. The performance of position estimation from Invariant Extended Kalman filter and odometry from LiDAR SLAM is compared with the default state estimator from Digit itself which are demonstrated through an experiment with ground truth reference.

Committee: Keith Redmill (Committee Member); Ayonga Hereid (Advisor) Subjects: Mechanical Engineering; Robotics

Bachelor of Science (BS), Ohio University, 2016, Computer Science

Mobile robots need to continuously navigate their environment. Doing so necessitates using sensor data to both map that environment and locate their position. A modular framework for performing 3D Simultaneous Localization and Mapping (SLAM) for use with indoor robots has been developed that addresses this problem. This framework was developed using a Microsoft Kinect TM sensor and works by extracting key points and features, matching the resulting key points and features, and using the matched key points to compute transforms that produce a consistent global map consisting of aligned point clouds. The accuracy of the resulting map can be affected by any of these portions of the algorithm. This thesis discusses the design of this SLAM framework and examines how different key point extraction and feature description algorithms affect the accuracy of the final map. This thesis evaluates FAST, SURF, SIFT and ORB key point extraction algorithms paired with BRIEF, ORB, SIFT, and SURF descriptors in the context of 3D VISUAL SLAM. Each of these 16 pairs of key point extraction and description algorithms were evaluated on two publically available datasets using three different metrics that act as measures of drift (accumulated error over time) and local and global consistency of the estimated map.

Committee: David Chelberg Ph.D. (Advisor) Subjects: Computer Science; Robotics

Master of Science (M.S.), University of Dayton, 2015, Electrical Engineering

Adding texture to an object is extremely important for the enhancement of the 3D model's visual realism. This thesis presents a new method for mapping texture onto a 3D face model. The complete architecture of the new method is described. In this thesis, there are two main parts, one is 3D mesh modifying and the other is image processing. In 3D mesh modifying part, we use one face obj file as the 3D mesh file. Based on the coordinates and indices of that file, a 3D face wireframe can be displayed on screen by using OpenGL API. The most common method for mapping texture onto 3D mesh is to do mesh parametrization. To achieve this goal, a perspective-projection method is used to map 3D mesh to a 2D plane. To improve the degree of the accuracy, we separates the 3D mesh into three pieces based on three different view positions from left to right. In image processing part, we extracted the face information from the green back- ground images by using image segmentation. Because of the three face images from different view positions, so they have different light illumination. In this thesis, a button controller was made to control the light illumination of three parts separately. The image blending method was used to reduce the texture seam between two different parts of the mesh. The proposed method in this thesis is new way to add detail to a 3D model. It provides a valid texture mapping, also satisfies the man-machine interaction exactly. Even if the images are taken under the different illumination, users can use keyboard to change its illumination for color matching. This new way provides a new method to parametrize and modify the mesh so as to be used for texture mapping.

Committee: John S. Loomis Dr. (Advisor) Subjects: Computer Engineering; Computer Science; Electrical Engineering

Doctor of Philosophy (PhD), Ohio University, 2015, Electrical Engineering (Engineering and Technology)

As the potential use of autonomous unmanned aerial vehicles (UAVs) has become more prevalent in both the public and private sectors, the need for a reliable three-dimensional (3D) positioning, navigation, and mapping (PNM) capability will be required to enable operation of these platforms in challenging environments where the Global Positioning System (GPS) may not necessarily be available. Especially, when the platform's operational scenario involves motion through different environments like outdoor open-sky, outdoor under foliage, outdoor-urban and indoor, and includes transitions between these environments, there may not be one particular method to solve the PNM problem. In this dissertation we are not solving the PNM problem for every possible environment, but select a couple of dissimilar sensor technologies to design and implement an integrated navigation and mapping method that can support reliable operation in an outdoor and structured indoor environment. The integrated navigation and mapping design is based on a Global Positioning System (GPS) receiver, an Inertial Measurement Unit (IMU), a monocular digital camera, and three short to medium range laser scanners. To evaluate the developed algorithms a hexacopter was built, equipped with the above sensors, and both hand-carried and flown through the target environments. This dissertation will show that dm-level relative positioning accuracies can be achieved for operations traversing a building, and that when segments are included where GPS is available, the platform's trajectory and map will be globally anchored with m-level accuracy.

Committee: Maarten Uijt de Haag (Advisor); Frank van Graas (Committee Member); Wouter Pelgrum (Committee Member); Douglas Lawrence (Committee Member) Subjects: Electrical Engineering

Master of Science in Engineering (MSEgr), Wright State University, 2012, Electrical Engineering

The objective of this thesis is to present a mapping algorithm for autonomous navigation of a lawnmower based on the range and bearing information of a SICK laser. The Simultaneous Localization and Mapping (SLAM) algorithm is a tool that can be used to navigate unmanned vehicle in an unknown environment. The two-dimensional (2D) maps of the obstacles can be obtained if the laser scans the environment in a plane horizontal to the ground. However, the 2D map does not give information of the objects that are placed below the height of the laser. This makes it difficult for the lawn mower to navigate the field since one of the objects (flower bed) is placed below the height of the laser. The computational complexity of the SLAM also makes it difficult to implement the algorithm on a low cost lawn mower. This thesis presents a mapping algorithm to map the environment that contains objects at a height below the laser. The localization of the unmanned vehicle is obtained from GPS (Global Positioning System) and IMU (Inertial Measurement Unit). The algorithm is simple and easy to implement as compared to the SLAM algorithm and was validated on the lawn mower. The position and dimensions of the flowerbed obtained using this algorithm closely matched the actual position from a reference point and dimensions of the flowerbed.

Committee: Kuldip Rattan PhD (Advisor); Xiaodong Zhang PhD (Committee Member); Marian Kazimierczuk PhD (Committee Member) Subjects: Electrical Engineering

Master of Science (MS), Wright State University, 2007, Computer Science

Analysis of large scale volumetric data sets is very important from the researcher's and technological point of view as it helps in understanding and analyzing the original data. For example, when analyzing volumetric images from cast arteries of pigs, it is essential to apply a suitable segmentation in order to retrieve morphometric data from the scanned image. Due to improved scanning technology, such as MicroCT, these scanned images can get as large as several gigabytes in size. In order to analyze such large scale data sets on common desktop computers is a challenge due to limited main memory. By using out-of-core techniques, where the hard disk is used as main storage medium while the main memory serves as cache, it is possible to process any size data efficiently on a off-the-shelf PC with limited memory. Filters have been implemented and are applied to the original data to process and transform the original data into another form which can be further analyzed. The presented method is not limited in terms of data set size. The data set size that can be processed is only limited by the size of the hard disk. Hence, the novelty of the described technique is the ability to apply the implemented filters to data sets of almost unlimited size using common, off-the-shelf desktop computers. In the second part of the thesis work, an effort has been made to automate the 3-D reconstruction of bi-planar images using epipolar approach. The standard approach would be to find the matching features in both the images called corresponding points. Then, from such a correspondence, depth can be easily calculated using standard triangulation method. This type of classical method would require careful selection of the matching features. Here we propose a technique which does not involve selection of matching features which requires manual intervention thereby automating the process. This approach produces reasonable results for the calibrated images due to the projection.

Committee: Thomas Wischgoll (Advisor) Subjects: Computer Science

Master of Architecture, Miami University, 2001, Architecture and Interior Design

It is imperative to have visual imagery when designing for lighting, therefore most architects of the past and present have used sketches, painting, or physical models for determining if the light in a space meets the design intentions. These tools all have their limitations. The advancement of computer technology, computer light simulation software, such as Radiance, Lightscape, etc., can be used as an effective tool for thematic daylight design. This thesis focuses on Lightscape, because of its reputation and affordability. The work found the tool to have variable precision, but a lot of research effort is focused on understanding what “precision” is necessary in a design context. It also strives to develop a method to incorporate Lightscape in the architectural design process.

Committee: Murali Paranandi (Advisor) Subjects: Architecture