Linear Matrix Inequalities (LMIs) are among the most powerful tools in modern control theory. They appear in applications ranging from stability analysis and robust control to Model Predictive Control (MPC) and optimal controller design.

BLOB Analysis, which stands for “Binary Large Object Analysis”, is a crucial topic in image processing and computer vision. It involves the grouping of pixels in an image based on connectivity and shared properties, such as intensity or color. A key component of BLOB extraction is Connected Component Analysis (CCA) [1], which refers to the process of identifying and labeling connected components in a binary image. In this post, I will discuss CCA and how to extract objects from an image.

I’ve always been fascinated by making things work efficiently, especially in designing control systems. One area that I want to talk about to day is Linear Programming (LP), which is a key aspect of optimization theory. Optimization techniques are widely used in control systems such as Linear Quadratic Regulator (LQR) and Model Predictive Control (MPC).

When writing functions in C and C++, we often need to pass information into the function to perform specific tasks. This process is known as parameter passing.