In this work, an experimental implementation of a chaotic oscillator is proposed. Many works have been proposed in order to solve some different interesting problems, such that determining structural damages in materials or for secure communications purpose. However, several approaches are in continuous-time, whereas in many cases, the systems are desired to be in discrete-time mode and in real time. In this work we present the implementation of one chaotic oscillator (Lorenz system) into a simple microcontroller, the implementation consists only of a low cost microcontroller, a few resistors and a simple algorithm. The algorithm could be easily modified to add or substitute the chaotic system

Visualization of blood vessels is a fundamental task in the evaluation of the health and biological integrity of tissue. Laser speckle contrast imaging (LSCI) is a non-invasive technique to determine the blood flow in superficial or exposed vasculature. However, the high scattering of biological tissue hinders the visualization of those structures. In this paper, we propose the use of principal component analysis (PCA) in combination with LSCI to improve the visualization of deep blood vessels by selecting the most significant principal components. This analysis was applied to in vitro samples, and our results demonstrate that this approach allows for the visualization and localization of blood vessels as deep as 1000 μm.

Visualization of deep blood vessels in speckle images is an important task as it is used to analyze the dynamics of the blood flow and the health status of biological tissue. Laser speckle imaging is a wide-field optical technique to measure relative blood flow speed based on the local speckle contrast analysis. However, it has been reported that this technique is limited to certain deep blood vessels (about $ρ=300μm$) because of the high scattering of the sample; beyond this depth, the quality of the vessel’s image decreases. The use of a representation based on homogeneity values, computed from the co-occurrence matrix, is proposed as it provides an improved vessel definition and its corresponding diameter. Moreover, a methodology is proposed for automatic blood vessel location based on the kurtosis analysis. Results were obtained from the different skin phantoms, showing that it is possible to identify the vessel region for different morphologies, even up to $900μm$ in depth.