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- All Subjects: Electrical Engineering
Description
A human communications research project at Arizona State University aurally
recorded the daily interactions of aware and consenting employees and their visiting
clients at the Software Factory, a software engineering consulting team, over a three
year period. The resulting dataset contains valuable insights on the communication
networks that the participants formed however it is far too vast to be processed manually
by researchers. In this work, digital signal processing techniques are employed
to develop a software toolkit that can aid in estimating the observable networks contained
in the Software Factory recordings. A four-step process is employed that starts
with parsing available metadata to initially align the recordings followed by alignment
estimation and correction. Once aligned, the recordings are processed for common
signals that are detected across multiple participants’ recordings which serve as a
proxy for conversations. Lastly, visualization tools are developed to graphically encode
the estimated similarity measures to efficiently convey the observable network
relationships to assist in future human communications research.
recorded the daily interactions of aware and consenting employees and their visiting
clients at the Software Factory, a software engineering consulting team, over a three
year period. The resulting dataset contains valuable insights on the communication
networks that the participants formed however it is far too vast to be processed manually
by researchers. In this work, digital signal processing techniques are employed
to develop a software toolkit that can aid in estimating the observable networks contained
in the Software Factory recordings. A four-step process is employed that starts
with parsing available metadata to initially align the recordings followed by alignment
estimation and correction. Once aligned, the recordings are processed for common
signals that are detected across multiple participants’ recordings which serve as a
proxy for conversations. Lastly, visualization tools are developed to graphically encode
the estimated similarity measures to efficiently convey the observable network
relationships to assist in future human communications research.
ContributorsPressler, Daniel (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
Machine learning (ML) has played an important role in several modern technological innovations and has become an important tool for researchers in various fields of interest. Besides engineering, ML techniques have started to spread across various departments of study, like health-care, medicine, diagnostics, social science, finance, economics etc. These techniques require data to train the algorithms and model a complex system and make predictions based on that model. Due to development of sophisticated sensors it has become easier to collect large volumes of data which is used to make necessary hypotheses using ML. The promising results obtained using ML have opened up new opportunities of research across various departments and this dissertation is a manifestation of it. Here, some unique studies have been presented, from which valuable inference have been drawn for a real-world complex system. Each study has its own unique sets of motivation and relevance to the real world. An ensemble of signal processing (SP) and ML techniques have been explored in each study. This dissertation provides the detailed systematic approach and discusses the results achieved in each study. Valuable inferences drawn from each study play a vital role in areas of science and technology, and it is worth further investigation. This dissertation also provides a set of useful SP and ML tools for researchers in various fields of interest.
ContributorsDutta, Arindam (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Richmond, Christ (Committee member) / Corman, Steven (Committee member) / Arizona State University (Publisher)
Created2018
Description
Alzheimer's disease is the 6th leading cause of death in the United States and vastly affects millions across the world each year. Currently, there are no medications or treatments available to slow or stop the progression of Alzheimer’s Disease. The GENUS therapy out of the Massachusetts Institute of Technology presently shows positive results in slowing the progression of the disease among animal trials. This thesis is a continuation of that study, to develop and build a testing apparatus for human clinical trials. Included is a complete outline into the design, development, testing measures, and instructional aid for the final apparatus.
ContributorsScheller, Rachel D (Author) / Bliss, Daniel (Thesis director) / Corman, Steven (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-12