You are here
Search results
(1 - 8 of 8)
- Title
- LIDAR AND CAMERA CALIBRATION USING A MOUNTED SPHERE
- Creator
- Li, Jiajia
- Date
- 2020
- Collection
- Electronic Theses & Dissertations
- Description
-
Extrinsic calibration between lidar and camera sensors is needed for multi-modal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a hand-held colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically...
Show moreExtrinsic calibration between lidar and camera sensors is needed for multi-modal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a hand-held colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically localize the sphere relative to the camera and the lidar. Then using the localization covariance models, the relative pose between the camera and lidar is derived. To evaluate the accuracy of our method, we record image and lidar data of a sphere at a set of known grid positions by using two rails mounted on a wall. The accurate calibration results are demonstrated by projecting the grid centers into the camera image plane and finding the error between these points and the hand-labeled sphere centers.
Show less
- Title
- SOCIAL MECHANISMS OF LEADERSHIP EMERGENCE : A COMPUTATIONAL EVALUATION OF LEADERSHIP NETWORK STRUCTURES
- Creator
- Griffin, Daniel Jacob
- Date
- 2020
- Collection
- Electronic Theses & Dissertations
- Description
-
Leadership emergence is a topic of immense interest in the organizational sciences. One promising recent development in the leadership literature focuses on the development and impact of informal leadership structures in a share leadership paradigm. Despite its theoretical importance, the network perspective of leadership emergence is still underdeveloped, largely due to the complexity of studying and theorizing about network-level phenomena. Using computational modeling techniques, I...
Show moreLeadership emergence is a topic of immense interest in the organizational sciences. One promising recent development in the leadership literature focuses on the development and impact of informal leadership structures in a share leadership paradigm. Despite its theoretical importance, the network perspective of leadership emergence is still underdeveloped, largely due to the complexity of studying and theorizing about network-level phenomena. Using computational modeling techniques, I evaluate the network-level implications of two existing theories that broadly represent social theories of leadership emergence. I derive formal representations for both foundational theories and expand on this theory to develop a synthesis theory describing how these two processes work in parallel. Results from simulated experiments indicate that group homogeneity is associated with vastly different leadership network structures depending on which theoretical process mechanisms are in play. This thesis contributes significantly to the literature by 1) advancing a network-based approach to leadership emergence research, 2) testing the implications of existing theory, 3) developing new theory, and 4) providing a strong foundation and tool kit for future leadership network emergence research.
Show less
- Title
- MICROBLOG GUIDED CRYPTOCURRENCY TRADING AND FRAMING ANALYSIS
- Creator
- Pawlicka Maule, Anna Paula
- Date
- 2020
- Collection
- Electronic Theses & Dissertations
- Description
-
With 56 million people actively trading and investing in cryptocurrency online and globally, there is an increasing need for an automatic social media analysis tool to help understand trading discourse and behavior. Previous works have shown the usefulness of modeling microblog discourse for the prediction of trading stocks and their price fluctuations, as well as content framing. In this work, I present a natural language modeling pipeline that leverages language and social network behaviors...
Show moreWith 56 million people actively trading and investing in cryptocurrency online and globally, there is an increasing need for an automatic social media analysis tool to help understand trading discourse and behavior. Previous works have shown the usefulness of modeling microblog discourse for the prediction of trading stocks and their price fluctuations, as well as content framing. In this work, I present a natural language modeling pipeline that leverages language and social network behaviors for the prediction of cryptocurrency day trading actions and their associated framing patterns. Specifically, I present two modeling approaches. The first determines if the tweets of a 24-hour period can be used to guide day trading behavior, specifically if a cryptocurrency investor should buy, sell, or hold their cryptocurrencies in order to make a trading profit. The second is an unsupervised deep clustering approach to automatically detect framing patterns. My contributions include the modeling pipeline for this novel task, a new dataset of cryptocurrency-related tweets from influential accounts, and a transaction volume dataset. The experiments executed show that this weakly-supervised trading pipeline achieves an 88.78% accuracy for day trading behavior predictions and reveals framing fluctuations prior to and during the COVID-19 pandemic that could be used to guide investment actions.
Show less
- Title
- Energy Conservation in Heterogeneous Smartphone Ad Hoc Networks
- Creator
- Mariani, James
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
In recent years mobile computing has been rapidly expanding to the point that there are now more devices than there are people. While once it was common for every household to have one PC, it is now common for every person to have a mobile device. With the increased use of smartphone devices, there has also been an increase in the need for mobile ad hoc networks, in which phones connect directly to each other without the need for an intermediate router. Most modern smart phones are equipped...
Show moreIn recent years mobile computing has been rapidly expanding to the point that there are now more devices than there are people. While once it was common for every household to have one PC, it is now common for every person to have a mobile device. With the increased use of smartphone devices, there has also been an increase in the need for mobile ad hoc networks, in which phones connect directly to each other without the need for an intermediate router. Most modern smart phones are equipped with both Bluetooth and Wifi Direct, where Wifi Direct has a better transmission range and rate and Bluetooth is more energy efficient. However only one or the other is used in a smartphone ad hoc network. We propose a Heterogeneous Smartphone Ad Hoc Network, HSNet, a framework to enable the automatic switching between Wifi Direct and Bluetooth to emphasize minimizing energy consumption while still maintaining an efficient network. We develop an application to evaluate the HSNet framework which shows significant energy savings when utilizing our switching algorithm to send messages by a less energy intensive technology in situations where energy conservation is desired. We discuss additional features of HSNet such as load balancing to help increase the lifetime of the network by more evenly distributing slave nodes among connected master nodes. Finally, we show that the throughput of our system is not affected due to technology switching for most scenarios. Future work of this project includes exploring energy efficient routing as well as simulation/scale testing for larger and more diverse smartphone ad hoc networks.
Show less
- Title
- AN EVOLUTIONARY MULTI-OBJECTIVE APPROACH TO SUSTAINABLE AGRICULTURAL WATER AND NUTRIENT OPTIMIZATION
- Creator
- Kropp, Ian Meyer
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
One of the main problems that society is facing in the 21st century is that agricultural production must keep pace with a rapidly increasing global population in an environmentally sustainable manner. One of the solutions to this global problem is a system approach through the application of optimization techniques to manage farm operations. However, unlike existing agricultural optimization research, this work seeks to optimize multiple agricultural objectives at once via multi-objective...
Show moreOne of the main problems that society is facing in the 21st century is that agricultural production must keep pace with a rapidly increasing global population in an environmentally sustainable manner. One of the solutions to this global problem is a system approach through the application of optimization techniques to manage farm operations. However, unlike existing agricultural optimization research, this work seeks to optimize multiple agricultural objectives at once via multi-objective optimization techniques. Specifically, the algorithm Unified Non-dominated Sorting Genetic Algorithm-III (U-NSGA-III) searched for irrigation and nutrient management practices that minimized combinations of environmental objectives (e.g., total irrigation applied, total nitrogen leached) while maximizing crop yield for maize. During optimization, the crop model named the Decision Support System for Agrotechnology Transfer (DSSAT) calculated the yield and nitrogen leaching for each given management practices. This study also developed a novel bi-level optimization framework to improve the performance of the optimization algorithm, employing U-NSGA-III on the upper level and Monte Carlo optimization on the lower level. The multi-objective optimization framework resulted in groups of equally optimal solutions that each offered a unique trade-off among the objectives. As a result, producers can choose the one that best addresses their needs among these groups of solutions, known as Pareto fronts. In addition, the bi-level optimization framework further improved the number, performance, and diversity of solutions within the Pareto fronts.
Show less
- Title
- Coreference Resolution for Downstream NLP Tasks
- Creator
- Pani, Sushanta Kumar
- Date
- 2021
- Collection
- Electronic Theses & Dissertations
- Description
-
Natural Language Processing (NLP) tasks have witnessed a significant improvement in performance by utilizing the power of end-to-end neural network models. An NLP system built for one job can contribute to other closely related tasks. Coreference Resolution (CR) systems work on resolving references and are at the core of many NLP tasks. The coreference resolution refers to the linking of repeated object references in a text. CR systems can boost the performance of downstream NLP tasks, such...
Show moreNatural Language Processing (NLP) tasks have witnessed a significant improvement in performance by utilizing the power of end-to-end neural network models. An NLP system built for one job can contribute to other closely related tasks. Coreference Resolution (CR) systems work on resolving references and are at the core of many NLP tasks. The coreference resolution refers to the linking of repeated object references in a text. CR systems can boost the performance of downstream NLP tasks, such as Text Summarization, Question Answering, Machine Translation, etc. We provide a detailed comparative error analysis of two state-of-the-art coreference resolution systems to understand error distribution in the predicted output. The understanding of error distribution is helpful to interpret the system behavior. Eventually, this will contribute to the selection of an optimal CR system for a specific target task.
Show less
- Title
- PRECISION DIAGNOSTICS AND INNOVATIONS FOR PLANT BREEDING RESEARCH
- Creator
- Hugghis, Eli
- Date
- 2021
- Collection
- Electronic Theses & Dissertations
- Description
-
Major technological advances are necessary to reach the goal of feeding our world’s growing population. To do this, there is an increasing demand within the agricultural field for rapid diagnostic tools to improve the efficiency of current methods in plant disease and DNA identification. The use of gold nanoparticles has emerged as a promising technology for a range of applications from smart agrochemical delivery systems to pathogen detection. In addition to this, advances in image...
Show moreMajor technological advances are necessary to reach the goal of feeding our world’s growing population. To do this, there is an increasing demand within the agricultural field for rapid diagnostic tools to improve the efficiency of current methods in plant disease and DNA identification. The use of gold nanoparticles has emerged as a promising technology for a range of applications from smart agrochemical delivery systems to pathogen detection. In addition to this, advances in image classification analyses have allowed machine learning approaches to become more accessible to the agricultural field. Here we present the use of gold nanoparticles (AuNPs) for the detection of transgenic gene sequences in maize and the use of machine learning algorithms for the identification and classification of Fusarium spp. infected wheat seed. AuNPs show promise in their ability to diagnose the presence of transgenic insertions in DNA samples within 10 minutes through colorimetric response. Image-based analysis with the utilization of logistic regression, support vector machines, and k-nearest neighbors were able to accurately identify and differentiate healthy and diseased wheat kernels within the testing set at an accuracy of 95-98.8%. These technologies act as rapid tools to be used by plant breeders and pathologists to improve their ability to make selection decisions efficiently and objectively.
Show less
- Title
- COMBINING FACE AND IRIS FOR PRIVACY PRESERVATION
- Creator
- Ledala, Achsah Junia
- Date
- 2021
- Collection
- Electronic Theses & Dissertations
- Description
-
With the extensive use of biometrics for authenticating users, the need to ensure privacy of biometric data is greater than ever before. Biometric authentication systems are vulnerable to attacks and the loss of biometric data will lead to loss of privacy of an individual. Multibiometrics refers to the use of multiple biometric modalities simultaneously in order to perform matching. In this work, we introduce a multibiometric fusion technique which can be used to ensure that the original raw...
Show moreWith the extensive use of biometrics for authenticating users, the need to ensure privacy of biometric data is greater than ever before. Biometric authentication systems are vulnerable to attacks and the loss of biometric data will lead to loss of privacy of an individual. Multibiometrics refers to the use of multiple biometric modalities simultaneously in order to perform matching. In this work, we introduce a multibiometric fusion technique which can be used to ensure that the original raw biometric data are unlikely to be compromised and, at the same time, recognition can be performed. The face and the iris biometric modalities are fused at the feature-level to produce discriminative embeddings that can be used for recognition. The original face or the iris cannot be retrieved from the combined representation, thus preserving the privacy of the individual. We present the results of this approach, provide analysis, discuss the challenges, and list possible future directions.
Show less