Matching Items (129)
Filtering by
- Resource Type: Text
Description
Graph theory is a critical component of computer science and software engineering, with algorithms concerning graph traversal and comprehension powering much of the largest problems in both industry and research. Engineers and researchers often have an accurate view of their target graph, however they struggle to implement a correct, and efficient, search over that graph.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
To facilitate rapid, correct, efficient, and intuitive development of graph based solutions we propose a new programming language construct - the search statement. Given a supra-root node, a procedure which determines the children of a given parent node, and optional definitions of the fail-fast acceptance or rejection of a solution, the search statement can conduct a search over any graph or network. Structurally, this statement is modelled after the common switch statement and is put into a largely imperative/procedural context to allow for immediate and intuitive development by most programmers. The Go programming language has been used as a foundation and proof-of-concept of the search statement. A Go compiler is provided which implements this construct.
ContributorsHenderson, Christopher (Author) / Bansal, Ajay (Thesis advisor) / Lindquist, Timothy (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2018
Description
UVLabel was created to enable radio astronomers to view and annotate their own data such that they could then expand their future research paths. It simplifies their data rendering process by providing a simple user interface to better access sections of their data. Furthermore, it provides an interface to track trends in their data through a labelling feature.
The tool was developed following the incremental development process in order to quickly create a functional and testable tool. The incremental process also allowed for feedback from radio astronomers to help guide the project's development.
UVLabel provides both a functional product, and a modifiable and scalable code base for radio astronomer developers. This enables astronomers studying various astronomical interferometric data labelling capabilities. The tool can then be used to improve their filtering methods, pursue machine learning solutions, and discover new trends. Finally, UVLabel will be open source to put customization, scalability, and adaptability in the hands of these researchers.
The tool was developed following the incremental development process in order to quickly create a functional and testable tool. The incremental process also allowed for feedback from radio astronomers to help guide the project's development.
UVLabel provides both a functional product, and a modifiable and scalable code base for radio astronomer developers. This enables astronomers studying various astronomical interferometric data labelling capabilities. The tool can then be used to improve their filtering methods, pursue machine learning solutions, and discover new trends. Finally, UVLabel will be open source to put customization, scalability, and adaptability in the hands of these researchers.
ContributorsLa Place, Cecilia (Author) / Bansal, Ajay (Thesis advisor) / Jacobs, Daniel (Thesis advisor) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2019
Description
Capturing the information in an image into a natural language sentence is
considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the fields of computer vision paired with natural language processing are supposed to be crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words which are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data.
The main focus of this thesis is to reduce the variance factor which will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in this thesis. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.
considered a difficult problem to be solved by computers. Image captioning involves not just detecting objects from images but understanding the interactions between the objects to be translated into relevant captions. So, expertise in the fields of computer vision paired with natural language processing are supposed to be crucial for this purpose. The sequence to sequence modelling strategy of deep neural networks is the traditional approach to generate a sequential list of words which are combined to represent the image. But these models suffer from the problem of high variance by not being able to generalize well on the training data.
The main focus of this thesis is to reduce the variance factor which will help in generating better captions. To achieve this, Ensemble Learning techniques have been explored, which have the reputation of solving the high variance problem that occurs in machine learning algorithms. Three different ensemble techniques namely, k-fold ensemble, bootstrap aggregation ensemble and boosting ensemble have been evaluated in this thesis. For each of these techniques, three output combination approaches have been analyzed. Extensive experiments have been conducted on the Flickr8k dataset which has a collection of 8000 images and 5 different captions for every image. The bleu score performance metric, which is considered to be the standard for evaluating natural language processing (NLP) problems, is used to evaluate the predictions. Based on this metric, the analysis shows that ensemble learning performs significantly better and generates more meaningful captions compared to any of the individual models used.
ContributorsKatpally, Harshitha (Author) / Bansal, Ajay (Thesis advisor) / Acuna, Ruben (Committee member) / Gonzalez-Sanchez, Javier (Committee member) / Arizona State University (Publisher)
Created2019
Description
The inception of the human-powered water pump began during my trip to Maasailand in Kenya over the Summer of 2017. Being one of the few Broadening the Reach of Engineering through Community Engagement (BRECE) Scholars at Arizona State University, I was given the opportunity to join Prescott College (PC) on their annual trip to the Maasai Education, Research, and Conservation (MERC) Institute in rural Kenya. The ASU BRECE scholars that choose to travel were asked to collaborate with the local Maasai community to help develop functional and sustainable engineering solutions to problems identified alongside community members using rudimentary technology and tools that were available in this resource-constrained setting. This initiative evolved into multiple projects from the installation of GravityLights (a local invention that powers LEDs with falling sandbags), the construction/installation of smokeless stoves, and development of a much-needed solution to move water from the rainwater collection tanks around camp to other locations. This last project listed was prototyped once in camp, and this report details subsequent iterations of this human-powered pump.
ContributorsMiller, Miles Edward (Author) / Henderson, Mark (Thesis director) / Abbas, James (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In this project, the use of deep neural networks for the process of selecting actions to execute within an environment to achieve a goal is explored. Scenarios like this are common in crafting based games such as Terraria or Minecraft. Goals in these environments have recursive sub-goal dependencies which form a dependency tree. An agent operating within these environments have access to low amounts of data about the environment before interacting with it, so it is crucial that this agent is able to effectively utilize a tree of dependencies and its environmental surroundings to make judgements about which sub-goals are most efficient to pursue at any point in time. A successful agent aims to minimizes cost when completing a given goal. A deep neural network in combination with Q-learning techniques was employed to act as the agent in this environment. This agent consistently performed better than agents using alternate models (models that used dependency tree heuristics or human-like approaches to make sub-goal oriented choices), with an average performance advantage of 33.86% (with a standard deviation of 14.69%) over the best alternate agent. This shows that machine learning techniques can be consistently employed to make goal-oriented choices within an environment with recursive sub-goal dependencies and low amounts of pre-known information.
ContributorsKoleber, Derek (Author) / Acuna, Ruben (Thesis director) / Bansal, Ajay (Committee member) / W.P. Carey School of Business (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The field of robotics is rapidly expanding, and with it, the methods of teaching and introducing students must also advance alongside new technologies. There is a challenge in robotics education, especially at high school levels, to expose them to more modern and practical robots. One way to bridge this gap is human-robot interaction for a more hands-on and impactful experience that will leave students more interested in pursuing the field. Our project is a Robotic Head Kit that can be used in an educational setting to teach about its electrical, mechanical, programming, and psychological concepts. We took an existing robot head prototype and further advanced it so it can be easily assembled while still maintaining human complexity. Our research for this project dove into the electronics, mechanics, software, and even psychological barriers present in order to advance the already existing head design. The kit we have developed combines the field of robotics with psychology to create and add more life-like features and functionality to the robot, nicknamed "James Junior." The goal of our Honors Thesis was to initially fix electrical, mechanical, and software problems present. We were then tasked to run tests with high school students to validate our assembly instructions while gathering their observations and feedback about the robot's programmed reactions and emotions. The electrical problems were solved with custom PCBs designed to power and program the existing servo motors on the head. A new set of assembly instructions were written and modifications to the 3D printed parts were made for the kit. In software, existing code was improved to implement a user interface via keypad and joystick to give students control of the robot head they construct themselves. The results of our tests showed that we were not only successful in creating an intuitive robot head kit that could be easily assembled by high school students, but we were also successful in programming human-like expressions that could be emotionally perceived by the students.
ContributorsRathke, Benjamin (Co-author) / Rivera, Gerardo (Co-author) / Sodemann, Angela (Thesis director) / Itagi, Manjunath (Committee member) / Engineering Programs (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
37,461 automobile accident fatalities occured in the United States in 2016 ("Quick Facts 2016", 2017). Improving the safety of roads has traditionally been approached by governmental agencies including the National Highway Traffic Safety Administration and State Departments of Transporation. In past literature, automobile crash data is analyzed using time-series prediction technicques to identify road segments and/or intersections likely to experience future crashes (Lord & Mannering, 2010). After dangerous zones have been identified road modifications can be implemented improving public safety. This project introduces a historical safety metric for evaluating the relative danger of roads in a road network. The historical safety metric can be used to update routing choices of individual drivers improving public safety by avoiding historically more dangerous routes. The metric is constructed using crash frequency, severity, location and traffic information. An analysis of publically-available crash and traffic data in Allgeheny County, Pennsylvania is used to generate the historical safety metric for a specific road network. Methods for evaluating routes based on the presented historical safety metric are included using the Mann Whitney U Test to evaluate the significance of routing decisions. The evaluation method presented requires routes have at least 20 crashes to be compared with significance testing. The safety of the road network is visualized using a heatmap to present distribution of the metric throughout Allgeheny County.
ContributorsGupta, Ariel Meron (Author) / Bansal, Ajay (Thesis director) / Sodemann, Angela (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
This thesis proposes the concept of soft robotic supernumerary limbs to assist the wearer in the execution of tasks, whether it be to share loads or replace an assistant. These controllable extra arms are made using soft robotics to reduce the weight and cost of the device, and are not limited in size and location to the user's arm as with exoskeletal devices. Soft robotics differ from traditional robotics in that they are made using soft materials such as silicone elastomers rather than hard materials such as metals or plastics. This thesis presents the design, fabrication, and testing of the arm, including the joints and the actuators to move them, as well as the design and fabrication of the human-body interface to unite man and machine. This prototype utilizes two types of pneumatically-driven actuators, pneumatic artificial muscles and fiber-reinforced actuators, to actuate the elbow and shoulder joints, respectively. The robotic limb is mounted at the waist on a backpack frame to avoid interfering with the wearer's biological arm. Through testing and evaluation, this prototype device proves the feasibility of soft supernumerary limbs, and opens up opportunities for further development into the field.
ContributorsOlson, Weston Roscoe (Author) / Polygerinos, Panagiotis (Thesis director) / Zhang, Wenlong (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
As robotics technology advances, robots are being created for use in situations where they collaborate with humans on complex tasks. For this to be safe and successful, it is important to understand what causes humans to trust robots more or less during a collaborative task. This research project aims to investigate human-robot trust through a collaborative game of logic that can be played with a human and a robot together. This thesis details the development of a game of logic that could be used for this purpose. The game of logic is based upon a popular game in AI research called ‘Wumpus World’. The original Wumpus World game was a low-interactivity game to be played by humans alone. In this project, the Wumpus World game is modified for a high degree of interactivity with a human player, while also allowing the game to be played simultaneously by an AI algorithm.
ContributorsBoateng, Andrew Owusu (Author) / Sodemann, Angela (Thesis director) / Martin, Thomas (Committee member) / Software Engineering (Contributor) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Adaptive expertise is a model of learning that posits two dimensions of development: efficiency and innovation. The mindset of an adaptive expert will serve any engineer by drawing upon diverse experiences to develop novel solutions to problems. Their mindset is based in lifelong learning, characterized by applying past experience to current design challenges. Solution design requires a process, and a breadth of experience is among the adaptive expert's greatest tools in identifying the approach to take in an unfamiliar situation. The fluidity and agility of their mind allows them to work effectively throughout their career in technical design, as the situation of an engineer's design work can vary drastically over the course of time. This paper describes a study on an innovative junior-level electrical and robotic systems project course taught at a large southwestern university that encourages students to develop adaptive expertise in the context of real-world design projects. By fabricating prototypes, students learn strategies for troubleshooting and technical design, and iterations of the part demand reflection on previous design thinking. This study seeks to answer the following research questions: (1) How does user-centered design stimulate abstractive design thinking? (2) How does fabrication of prototypes stimulate active design thinking? And (3) How is the classroom culture enabling engineering design in the optimal adaptability corridor? Critical incident interviews were conducted with stakeholders in the course, and a thematic analysis of the transcripts conducted. Results show that this project-based curriculum fosters adaptive expertise by stimulating both abstractive and active design thinking. This provides a framework for practicing adaptive design thinking in classrooms. Disseminating these findings to curriculum designers will encourage more engaging, effective classes that graduate adaptive experts.
ContributorsLarson, James Robert (Author) / Jordan, Shawn (Thesis director) / Lande, Micah (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05