Continuous Characterization of Universal Invertible Amplifier Using Source Noise
With passage of time and repeated usage of a system, component values that make up the system parameters change, causing errors in its functional output. In order to ensure the fidelity of the results derived from these systems it is thus very important to keep track of the system parameters while being used. This thesis introduces a method for tracking the existing system parameters while the system was being used using the inherent noise of its signal source. Kalman filter algorithm is used to track the inherent noise response to the system and use that response to estimate the system parameters. In this thesis this continuous characterization scheme has been used on a Universal Invertible Amplifier (UIA). Current biomedical research as well as diagnostic medicine depend a lot on shape profile of bio-electric signals of different sources, for example heart, muscle, nerve, brain etc. making it very important to capture the different event of these signals without the distortion usually introduced by the filtering of the amplifier system. The Universal Invertible Amplifier extracts the original signal in electrodes by inverting the filtered and compressed signal while its gain bandwidth profile allows it to capture from the entire bandwidth of bioelectric signals. For this inversion to be successful the captured compressed and filtered signals needs to be inverted with the actual system parameters that the system had during capturing the signals, not its original parameters. The continuous characterization scheme introduced in this thesis is aimed at knowing the system parameters of the UIA by tracking the response of its source noise and estimating its transfer function from that. Two types of source noises have been tried out in this method, an externally added noise that was digitally generated and a noise that inherently contaminates the signals the system is trying to capture. In our cases, the UIA was used to capture nerve activity from vagus nerve where the signal was contaminated with electrocardiogram signals providing us with a well-defined inherent noise whose response could be tracked with Kalman Filter and used to estimate the transfer function of UIA. The transfer function estimation using the externally added noise did not produce good results but could be improved by means that can be explored as future direction of this project. However continuous characterization using the inherent noise, a bioelectric signal, was successful producing transfer function estimates with minimal error. Thus this thesis was successful to introduce a novel approach for system characterization using bio-signal contamination.
Yoshida, Purdue University.
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