Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Electrical Engineering Program
This project explores the optimization of HVAC and renewable energy systems of new, modular and portable off grid systems like the Recycling Microfactory, a joint project between Arizona State University and the Department of Defense (DOD). There has been a growing push for innovative solutions to address the underlying deficiencies in United States supply chains and energy infrastructure. This paper seeks to elaborate on the proposed solutions of portable and modular infrastructure to support neglected sectors of the economy: energy grid modernization and waste management specifically. This will be done by analyzing the Microfactory’s operations and optimizing the site’s energy efficiency. Background knowledge and context behind the current state of supply chains and of both energy and waste management sectors are briefly explained in the introduction followed by a high-level overview of the concept of modular infrastructure such as the Recycling Microfactory. The body of the thesis is organized into two sections. The first section focuses on the methods for planning the structure, layout, and workflow of the Recycling Microfactory for when it is out for transport and organized for operation. A series of 3D parametric models were used for the high-fidelity layouts of the Microfactory and was developed in conjunction with user experience gained from evaluating the custom-built processing equipment. The second section further expands the initial energy simulation models of the Microfactory generated from the first simulations of the project. Utilizing the building energy modeling (BEM) software EnergyPlus/OpenStudio, more advanced models accounting for HVAC sizing requirements, climate building standards (i.e., building insulation), and human comfort standards for workspaces are generated. A more realistic simulation of the energy requirements of the Microfactory to maintain temperature and humidity standards is presented through a comprehensive review of the OpenStudio building model design flow.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.
This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.