UNIVERSITY OF UTAH
ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT
DISSERTATION DEFENSE FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
by
Anil Kumar Ram Rakhyani
Advisor: Gianluca Lazzi
Design and Optimization of Efficient Magnetic Coils for Biomedical Applications
Magnetic fields are permeable to the biological tissues and can induce electric field in the conductive structures. Prosthetic devices such as retinal implants use time-varying magnetic field to achieve wireless power transfer to the implanted magnetic coil. However, devices such as magnetic stimulators use the induction principle to create an electric field at the stimulation site. Efficiency of these devices is primarily dependent on the design of the magnetic coils. Therefore, in this work, we designed and validated efficient magnetic coils for wireless power transfer to implanted devices and magnetic stimulation of the peripheral nerves.
In this work, we proposed a multi-coil power transfer system which solves some of the current challenges. The proposed multi-coil WPT system achieves more than twice the power transfer efficiency, controllable voltage gain, wider frequency bandwidth, higher tolerance to coupling and load variations, lower absorbed power in the tissue and lower radiated field from the magnetic coil than a comparable conventional system.
Magnetic coils play an important role in controlling the distribution of induced electric field inside the nerve during magnetic stimulation. In this work, we developed anatomically correct tissue models to study the effect of tissue heterogeneity and the surrounding media on the induced electric field. We also developed an optimization algorithm for designing energy efficient cm-size magnetic coils, that were then used for ex-vivo magnetic stimulation of the frog’s sciatic nerve.
Thursday, April 24, 2014
10:00 a.m.
ECE Conference Room
3235 MEB
The public is invited
UNIVERSITY OF UTAH
ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT
THESIS DEFENSE FOR THE DEGREE OF
MASTER OF SCIENCE
by
Brad Mager
Advisor: Neal Patwari
Maintaining Accuracy of Device-Free Localization Systems in Changing Environments
Device-free localization (DFL) systems are used to locate a person in an environment by measuring the changes in received signal strength (RSS) on all the links in the network. A fingerprint-based DFL method, such as the type addressed in this thesis, collects a database of RSS fingerprints and uses a machine learning classifier to determine a person’s location. However, as the environment changes over time due to furniture or other objects being moved, the RSS fingerprints diverge further and further from those stored in the database, causing the accuracy of the system to suffer.
This thesis investigates the degradation over time of localization accuracy in RF sensor networks using a fingerprint-based method with a machine learning classifier. We perform extensive experiments that allow quantification of how changes in an environment affect accuracy, through a process of moving specific items in a residential home one at a time and conducting separate localization experiments after each change.
We find that the Random Forest classifier performs the best as changes are made, compared to three other classifiers tested. In addition, we present a correlation method for selecting the channel used with each link, which improves localization accuracy from an average of 95.2% to an overall 98.4% accuracy using Random Forest. We thus demonstrate that combining the Random Forest classifier with a correlation method of selecting a channel for each link offers a viable approach to developing a more robust system for device-free localization that is less susceptible to changes in the environment.
Tuesday, April 29, 2014
10:00 a.m.
ECE Conference Room
3235 MEB
The public is invited
Agenda
Posterboard 
Month 