This course is about Biological Signal and System Modeling.
Modeling is the main core of any signal and system processing and analysis. For
example, an image can be seen as a matrix (mathematical/deterministic model)
and in this base for a conventional processing such as denoising average operator
is used. However as we know this operator doesn’t lead to an optimum result (the
produced image is usually blurry). In another point of view, the image can be
seen as a random field (statistical model), and so the denoising process is converted
to an estimation problem and better denoising results is achieved. Similarly we
can solve the denoising problem by modeling an image using PDE, geometric and
graph-based methods, etc. These model can be also used in transform domain using
atomic representations such as x-let transforms (data preparation). The resulted
denoising process is completely depended on the proposed model for noise-free image.
In this base we will introduce various methods for modeling of biologic data including
mathematical/statistical models, energy-based models (variational and
PDE), transform-based models, and geometric and graph-based models.
After that we will focus on using these methods to model biomedical signals
- Teacher: Admin User