Abstract
To recognize biomedical signal, a problem that we often have to face is the noise in the real world. With the noise, biomedical signal will be different every time. For example, the heartbeat of one healthy person must be different a little every time, but we’ll not diagnose it is an abnormal heartbeat. So we have to consider the effect of the noise into the biomedical signal when we try to classify or recognize them. One kind of algorithm called Diffusion Network is suitable for this kind of condition. It includes the noise term in the equation. Once it learned the signal, the system can tollerance the noise interference in a certain extent. Another characteristic is the Diffusion Network considers the time-varying coefficient. The characteristic makes the system recognize the signal real time and it is a very strong point in the application. Unsatisfying at the learning and recognition of the signal, we try to implement the Diffusion Network into VLSI technology, which will be portable and convenient. Thinking about it, if there is a portable instrument which can learn or recognize the biomedical signal, it is how helpful for the medical treatment. The first step is to make the neurons in the Diffusion Network be a ciruit system. The problem we face is how to transfrom these equations into circuit construction and what is the mapping of the parameter between the mathematical value and the value in the circuit. After overcome these questions, we try to reconstruct the signals with the circuit system, and then compare the result with the mathematical computation. In the end, we implement the circuit into VLSI technology. From the chip testing, there are some errors between the chip and the simulation of the circuits. We discuss the reasons and try to modify them. After that, we hope the system can reconstruct the signals successfully.