ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at email@example.com.
- 3 Public
- 3 Electrical engineering
- 1 Artifacts Suppression
- 1 Clutter
- 1 Feature extraction
- 1 IoT
- 1 LTE
- 1 Low-power
- 1 Machine Learning
- 1 Multiple target tracking
- 1 NB-IoT
- 1 Neural activity
- 1 Physical-layer
- 1 Probability hypothesis density filter
- 1 Radar
- 1 Sequential Monte Carlo Methods
- 1 Systems science
- 1 Target Tracking
- 1 Unsupervised Clustering
- 1 Wireless communication
With the new age Internet of Things (IoT) revolution, there is a need to connect a wide range of devices with varying throughput and performance requirements. In this thesis, a wireless system is proposed which is targeted towards very low power, delay insensitive IoT applications with low throughput requirements. The low cost receivers for such devices will have very low complexity, consume very less power and hence will run for several years. Long Term Evolution (LTE) is a standard developed and administered by 3rd Generation Partnership Project (3GPP) for high speed wireless communications for mobile devices. As a part of …
- Sharma, Prashant, Bliss, Daniel, Chakrabarti, Chaitali, et al.
- Created Date
The radar performance of detecting a target and estimating its parameters can deteriorate rapidly in the presence of high clutter. This is because radar measurements due to clutter returns can be falsely detected as if originating from the actual target. Various data association methods and multiple hypothesis filtering approaches have been considered to solve this problem. Such methods, however, can be computationally intensive for real time radar processing. This work proposes a new approach that is based on the unsupervised clustering of target and clutter detections before target tracking using particle filtering. In particular, Gaussian mixture modeling is first used …
- Freeman, Matthew Gregory, Papandreou-Suppappola, Antonia, Bliss, Daniel, et al.
- Created Date
Neural activity tracking using electroencephalography (EEG) and magnetoencephalography (MEG) brain scanning methods has been widely used in the field of neuroscience to provide insight into the nervous system. However, the tracking accuracy depends on the presence of artifacts in the EEG/MEG recordings. Artifacts include any signals that do not originate from neural activity, including physiological artifacts such as eye movement and non-physiological activity caused by the environment. This work proposes an integrated method for simultaneously tracking multiple neural sources using the probability hypothesis density particle filter (PPHDF) and reducing the effect of artifacts using feature extraction and stochastic modeling. Unique …
- Jiang, Jiewei, Papandreou-Suppappola, Antonia, Bliss, Daniel, et al.
- Created Date