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 gradformat@asu.edu.
Filtering by
- All Subjects: Electrical Engineering
- Creators: Fan, Deliang
make motion energy harvesting a viable energy source. However, it has not been
widely adopted due to bulky energy harvester designs that are uncomfortable to wear. This
work addresses this problem by analyzing the feasibility of powering low wearable power
devices using piezoelectric energy generated at the human knee. We start with a novel
mathematical model for estimating the power generated from human knee joint movements.
This thesis’s major contribution is to analyze the feasibility of human motion energy harvesting
and validating this analytical model using a commercially available piezoelectric
module. To this end, we implemented an experimental setup that replicates a human knee.
Then, we performed experiments at different excitation frequencies and amplitudes with
two commercially available Macro Fiber Composite (MFC) modules. These experimental
results are used to validate the analytical model and predict the energy harvested as a function
of the number of steps taken in a day. The model estimates that 13μWcan be generated
on an average while walking with a 4.8% modeling error. The obtained results show that
piezoelectricity is indeed a viable approach for powering low-power wearable devices.
Motivated by the aforementioned concerns, this dissertation comprehensively investigates the emerging efficiency and security issues of DNNs, from both software and hardware design perspectives. From the efficiency perspective, as the foundation technique for efficient inference of target DNN, the model compression via quantization is elaborated. In order to maximize the inference performance boost, the deployment of quantized DNN on the revolutionary Computing-in-Memory based neural accelerator is presented in a cross-layer (device/circuit/system) fashion. From the security perspective, the well known adversarial attack is investigated spanning from its original input attack form (aka. Adversarial example generation) to its parameter attack variant.