2 resultados para giant spike germplasm
em AMS Tesi di Laurea - Alm@DL - Università di Bologna
Resumo:
Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.
Resumo:
Extra mixing at the borders of convective zones in stellar interiors takes on an important role in the chemical evolution of stars and galaxies through the transport of chemical elements towards the stellar surface: knowing the overshooting mechanism can therefore lead to a better understanding of the observed chemical abundances in stellar photospheres. The comprehension of this phenomenon is quite uncertain and currently object of many studies. In particular, concerning low mass stars, in the past decades several works highlighted a discrepancy between the observed luminosity of the Red-Giant Branch bump and its prediction from simulations, which can be fixed including overshooting at the base of the convective envelope. This work, studying the Red-Giant Branch bump and using it as a diagnostic for extra mixing processes, tries to classify two different types of overshooting, instantaneous and diffusive, using both simulations from stellar models and Globular Clusters’ data. The aim is to understand which one of the two mixing processes is the most suitable in reproducing the observed stellar behaviour and, in case both of them provide reliable results, what are the conditions under which they produce the same effects on the Red-Giant Branch bump luminosity function and are consequently indistinguishable. Finally, possible dependences of overshooting efficiency on stellar parameters, such as chemical composition, are analysed.