Effective potential approach to quantum dissipation in condensed matter systems

The effects of dissipation on the thermodynamic properties of nonlinear quantum systems are approached by the path-integral method in order to construct approximate classical-like formulas for evaluating thermal averages of thermodynamic quantities. Explicit calculations are presented for one-particle and many-body systems. The effects of the dissipation mechanism on the phase diagram of two-dimensional Josephson arrays is discussed.Comment: 7 pages, 5 figures, to appear in the Proceedings of Nonlinearity, Integrability And All That 20 Years After Needs 7

Simulating Quantum Dissipation in Many-Body Systems

An efficient Path Integral Monte Carlo procedure is proposed to simulate the behavior of quantum many-body dissipative systems described within the framework of the influence functional. Thermodynamic observables are obtained by Monte Carlo sampling of the partition function after discretization and Fourier transformation in imaginary time of the dynamical variables. The method is tested extensively for model systems, using realistic dissipative kernels. Results are also compared with the predictions of a recently proposed semiclassical approximation, thus testing the reliability of the latter approach for weak quantum coupling. Our numerical method opens the possibility to quantitatively describe real quantum dissipative systems as, e.g., Josephson junction arrays.Comment: 10 pages, 4 figure

Touch Position Detection in Electrical Tomography Tactile Sensors Through Quadratic Classifier

Traditional electrical tomography tactile sensors consider the usage of the system’s finite element model. This approach brings disadvantages that jeopardise their applicability aspect and wide use. To address this limitation, the main thrust of this work is to present a method for touch position identification for an electrical tomography flexible tactile sensor. This is done by using a supervised machine learning algorithm for performing classification, namely quadratic discriminant analysis. This approach provides accurate contact location identification, increasing the detection speed and the sensor versatility when compared to traditional electrical tomography approaches. Results obtained show classification accuracy rates of up to 91.6% on unseen test data and an average euclidean error ranging from 1 to 10 mm depending on the contact location over the sensor. The sensor is then applied in real case scenarios to show its efficiency. These outcomes are encouraging since they promote the future practical usage of flexible and low-cost sensors

A reaction-diffusion heart model for the closed-loop evaluation of heart-pacemaker interaction

The purpose of this manuscript is to develop a reaction-diffusion heart model for closed-loop evaluation of heart-pacemaker interaction, and to provide a hardware setup for the implementation of the closed-loop system. The heart model, implemented on a workstation, is based on the cardiac monodomain formulation and a phenomenological model of cardiac cells, which we fitted to the electrophysiological properties of the different cardiac tissues. We modelled the pacemaker as a timed automaton, deployed on an Arduino 2 board. The Arduino and the workstation communicate through a PCI acquisition board. Additionally, we developed a graphical user interface for easy handling of the framework. The myocyte model resembles the electrophysiological properties of atrial and ventricular tissue. The heart model reproduces healthy activation sequence and proved to be computationally efficient (i.e., 1 s simulation requires about 5 s). Furthermore, we successfully simulated the interaction between heart and pacemaker models in three well-known pathological contexts. Our results showed that the PDE formulation is appropriate for the simulation in closed-loop. While computationally more expensive, a PDE model is more flexible and allows to represent more complex scenarios than timed or hybrid automata. Furthermore, users can interact more easily with the framework thanks to the graphical representation of the spatiotemporal evolution of the membrane potentials. By representing the heart as a reaction-diffusion model, the proposed closed-loop system provides a novel and promising framework for the assessment of cardiac pacemakers

An Activity Classifier based on Heart Rate and Accelerometer Data Fusion

The European project ProeTEX realized a novel set of prototypes based on smart garments that integrate sensors for the real-time monitoring of physiological, activity-related and environmental parameters of the emergency operators during their interventions. The availability of these parameters and the emergency scenario suggest the implementation of novel classification methods aimed at detecting dangerous status of the rescuer automatically, and based not only on the classical activityrelated signals, rather on a combination of these data with the physiological status of the subject. Here we propose a heart rate and accelerometer data fusion algorithm for the activity classification of rescuers in the emergency context

Moving Auto-Correlation Window Approach for Heart Rate Estimation in Ballistocardiography Extracted by Mattress-Integrated Accelerometers

Continuous heart monitoring is essential for early detection and diagnosis of cardiovascular diseases, which are key factors for the evaluation of health status in the general population. Therefore, in the future, it will be increasingly important to develop unobtrusive and transparent cardiac monitoring technologies for the population. The possible approaches are the development of wearable technologies or the integration of sensors in daily-life objects. We developed a smart bed for monitoring cardiorespiratory functions during the night or in the case of continuous monitoring of bedridden patients. The mattress includes three accelerometers for the estimation of the ballistocardiogram (BCG). BCG signal is generated due to the vibrational activity of the body in response to the cardiac ejection of blood. BCG is a promising technique but is usually replaced by electrocardiogram due to the difficulty involved in detecting and processing the BCG signals. In this work, we describe a new algorithm for heart parameter extraction from the BCG signal, based on a moving auto-correlation sliding-window. We tested our method on a group of volunteers with the simultaneous co-registration of electrocardiogram (ECG) using a single-lead configuration. Comparisons with ECG reference signals indicated that the algorithm performed satisfactorily. The results presented demonstrate that valuable cardiac information can be obtained from the BCG signal extracted by low cost sensors integrated in the mattress. Thus, a continuous unobtrusive heart-monitoring through a smart bed is now feasible