Synchronization in Von Bertalanffy’s models

Many data have been useful to describe the growth of marine mammals, invertebrates and reptiles, seabirds, sea turtles and fishes, using the logistic, the Gom-pertz and von Bertalanffy's growth models. A generalized family of von Bertalanffy's maps, which is proportional to the right hand side of von Bertalanffy's growth equation, is studied and its dynamical approach is proposed. The system complexity is measured using Lyapunov exponents, which depend on two biological parameters: von Bertalanffy's growth rate constant and the asymptotic weight. Applications of synchronization in real world is of current interest. The behavior of birds ocks, schools of fish and other animals is an important phenomenon characterized by synchronized motion of individuals. In this work, we consider networks having in each node a von Bertalanffy's model and we study the synchronization interval of these networks, as a function of those two biological parameters. Numerical simulation are also presented to support our approaches.

Personalized public transportation information

Thesis to obtain the Master of Science Degree in Computer Science and Engineering

Off-the-person electrocardiography: performance assessment and clinical correlation

Previous work by our group introduced a novel concept and sensor design for “off-the-person” ECG, for which evidence on how it compares against standard clinical-grade equipment has been largely missing. Our objectives with this work are to characterise the off-the-person approach in light of the current ECG systems landscape, and assess how the signals acquired using this simplified setup compare with clinical-grade recordings. Empirical tests have been performed with real-world data collected from a population of 38 control subjects, to analyze the correlation between both approaches. Results show off-the-person data to be correlated with clinical-grade data, demonstrating the viability of this approach to potentially extend preventive medicine practices by enabling the integration of ECG monitoring into multiple dimensions of people’s everyday lives. © 2015, IUPESM and Springer-Verlag Berlin Heidelberg.

Embedded system for individual recognition based on ECG biometrics

Biometric recognition is emerging has an alternative solution for applications where the privacy of the information is crucial. This paper presents an embedded biometric recognition system based on the Electrocardiographic signals (ECG) for individual identification and authentication. The proposed system implements a real-time state-of-the-art recognition algorithm, which extracts information from the frequency domain. The system is based on a ARM Cortex 4. Preliminary results show that embedded platforms are a promising path for the implementation of ECG-based applications in real-world scenario.