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Description
Lighting Audio is a team of senior electrical engineering students at the Arizona State University mentored by Director Emeritus Professor Ronald Roedel and 2nd Committee Member George Karady attempting to prove the feasibility of a consumer grade plasma arc speaker. The plasma arc speaker is a project that explores the use of high voltage arcs to produce audible sound amplification. The goal of the project is to prove feasibility that a consumer grade plasma arc speaker could exist in the marketplace. The inherent challenge was producing audio amplification that could compete with current loudspeakers all while ensuring user safety from the hazards of high voltage and current shock, electromagnetic damage, and ozone from the plasma arc. The project has thus far covered the process of design conception to realization of a prototype device. The operation of the plasma arc speaker is based on the high voltage plasma arc created between two electrodes. The plasma arc rapidly heats and cools the surrounding air creating changes in air pressure which vibrate the air. These pockets of pressurized air are heard as sound. The circuit incorporates a flyback transformer responsible for creating the high voltage necessary for arcing.
ContributorsNandan, Rahul S (Author) / Roedel, Ronald (Thesis director) / Huffman, James (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2014-05
Description
A high voltage plasma arc can be created and sustained in air by subjecting the gases to an electric field with high voltage potential, causing ionization. The internal energy of the ionized gases can be transferred to corresponding pressure waves when the matter involved switches between the gaseous and plasma states. By pulse-width modulating a transformer driving signal, the transfer of internal electrical energy to resonating pressure waves may be controlled. Audio wave input to the driver signal can then be modulated into the carrier wave and be used to determine the width of each pulse in the plasma, thus reconstructing the audio signal as pressure, or sound waves, as the plasma arc switches on and off. The result will be the audio waveform resonating out of the plasma arc as audible sound, and thus creating a plasma loudspeaker. Theory of operation was tested through construction of a plasma arc speaker, and resultant audio playback was analyzed. This analysis confirmed accurate reproduction of audio signal in audible sound.
ContributorsBoehringer, Brian Thomas (Author) / Roedel, Ronald (Thesis director) / Huffman, James (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-05
Description
Lighting Audio is a team of senior electrical engineering students at the Arizona State University mentored by Director Emeritus Professor Ronald Roedel and 2nd Committee Member George Karady attempting to prove the feasibility of a consumer grade plasma arc speaker. The plasma arc speaker is a project that explores the use of high voltage arcs to produce audible sound amplification. The goal of the project is to prove feasibility that a consumer grade plasma arc speaker could exist in the marketplace. The inherent challenge was producing audio amplification that could compete with current loudspeakers all while ensuring user safety from the hazards of high voltage and current shock, electromagnetic damage, and ozone from the plasma arc. The project has thus far covered the process of design conception to realization of a prototype device. The operation of the plasma arc speaker is based on the high voltage plasma arc created between two electrodes. The plasma arc rapidly heats and cools the surrounding air creating changes in air pressure which vibrate the air. These pockets of pressurized air are heard as sound. The circuit incorporates a flyback transformer responsible for creating the high voltage necessary for arcing.
ContributorsNandan, Rahul S (Author) / Roedel, Ronald (Thesis director) / Huffman, James (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-05
Description
In natural language processing, language models have achieved remarkable success over the last few years. The Transformers are at the core of most of these models. Their success can be mainly attributed to an enormous amount of curated data they are trained on. Even though such language models are trained on massive curated data, they often need specific extracted knowledge to understand better and reason. This is because often relevant knowledge may be implicit or missing, which hampers machine reasoning. Apart from that, manual knowledge curation is time-consuming and erroneous. Hence, finding fast and effective methods to extract such knowledge from data is important for improving language models. This leads to finding ideal ways to utilize such knowledge by incorporating them into language models. Successful knowledge extraction and integration lead to an important question of knowledge evaluation of such models by developing tools or introducing challenging test suites to learn about their limitations and improve them further. So to improve the transformer-based models, understanding the role of knowledge becomes important. In the pursuit to improve language models with knowledge, in this dissertation I study three broad research directions spanning across the natural language, biomedical and cybersecurity domains: (1) Knowledge Extraction (KX) - How can transformer-based language models be leveraged to extract knowledge from data? (2) Knowledge Integration (KI) - How can such specific knowledge be used to improve such models? (3) Knowledge Evaluation (KE) - How can language models be evaluated for specific skills and understand their limitations?
I propose methods to extract explicit textual, implicit structural, missing textual, and missing structural knowledge from natural language and binary programs using transformer-based language models. I develop ways to improve the language model’s multi-step and commonsense reasoning abilities using external knowledge. Finally, I develop challenging datasets which assess their numerical reasoning skills in both in-domain and out-of-domain settings.
ContributorsPal, Kuntal Kumar (Author) / Baral, Chitta (Thesis advisor) / Wang, Ruoyu (Committee member) / Blanco, Eduardo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023