The Partial Product Structure Adaptive Filter for Biomedical Signal Processing

In this thesis, we proposed a Novel Partial Product Structure (PPS) Adaptive Filter, which is used in processing the biomedical signal from human physiology. This PPS adaptive filter is part of a biochip which will be implanted into a human body.
In order to implant this biochip into human bodies, low power and small size are requested. We developed a Novel Partial Product Structure Adaptive Filter. This PPS adaptive filter is suitable for biomedical signal processing. We used folding algorithm, Horner’s rule, and Tree-height algorithm to optimize the area size. So, this PPS adaptive filter has optimized area size and acceptable operation speed for biomedical signal processing.
In order to simulate this PPS adaptive filter, we choose noise canceller as our target. Noise canceller is a wildly used adaptive application in biomedical signal processing, voice signal processing, and digital communication. Also, we used Matlab as our algorithm simulation tool, ModelSim as our RTL level simulation tool.
Finally, we used Altera Apex DSP Development Board to implement the conventional and PPS adaptive predictors. A comparison is introduced and shows that the proposed PPS algorithm has almost 30% reduction in chip size.