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.
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- All Subjects: engineering
- Creators: Ozev, Sule
This research mainly focus on the technique to track changes in the dynamic loop characteristics of the DC-DC converters without disturbing the normal mode of operation using a white noise based excitation and correlation. White noise excitation is generated via pseudo random disturbance at reference and PWM input of the converter with the test signal being spread over a wide bandwidth, below the converter noise and ripple floor. Test signal analysis is achieved by correlating the pseudo-random input sequence with the output response and thereby accumulating the desired behavior over time and pulling it above the noise floor of the measurement set-up. An off-the shelf power converter, LM27402 is used as the DUT for the experimental verification. Experimental results show that the proposed technique can estimate converter's natural frequency and Q-factor within ±2.5% and ±0.7% error margin respectively, over changes in load inductance and capacitance.
In today's global supply chain, any of these steps are prone to interference from rogue players, creating a security risk.
Manufactured devices need to be verified to perform only their intended operations since it is not economically feasible to control the supply chain and use only trusted facilities.
It is becoming increasingly necessary to trust but verify the received devices both at production and in the field.
Unauthorized hardware or firmware modifications, known as Trojans,
can steal information, drain the battery, or damage battery-driven embedded systems and lightweight Internet of Things (IoT) devices.
Since Trojans may be triggered in the field at an unknown instance,
it is essential to detect their presence at run-time.
However, it isn't easy to run sophisticated detection algorithms on these devices
due to limited computational power and energy, and in some cases, lack of accessibility.
Since finding a trusted sample is infeasible in general, the proposed technique is based on self-referencing to remove any effect of environmental or device-to-device variations in the frequency domain.
In particular, the self-referencing is achieved by exploiting the band-limited nature of Trojan activity using signal detection theory.
When the device enters the test mode, a predefined test application is run on the device
repetitively for a known period. The periodicity ensures that the spectral electromagnetic power of the test application concentrates at known frequencies, leaving the remaining frequencies within the operating bandwidth at the noise level. Any deviations from the noise level for these unoccupied frequency locations indicate the presence of unknown (unauthorized) activity. Hence, the malicious activity can differentiate without using a golden reference or any knowledge of the Trojan activity attributes.
The proposed technique's effectiveness is demonstrated through experiments with collecting and processing side-channel signals, such as involuntarily electromagnetic emissions and power consumption, of a wearable electronics prototype and commercial system-on-chip under a variety of practical scenarios.