Ricerc@Sapienza

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Pharmacologic and non-pharmacologic rhythm control strategies impact on AF-related symptoms, while leaving largely unaffected the risk of stroke. Moreover, up to 20% of AF patients are asymptomatic during paroxysmal relapses of arrhythmia, thus underlying the need for early markers to identify at-risk patients and prevent cerebrovascular accidents. Indeed, non-invasive assessment of pre-clinical substrate changes that predispose to AF could provide early identification of at-risk patients and allow for tailored care paths.

The paper focuses on the time domain output-only technique called Data-Driven Stochastic Subspace Identification (DD-SSI); in order to identify modal models (frequencies, damping ratios and mode shapes), the role of its user-defined parameters is studied, and rules to determine their minimum values are proposed. Such investigation is carried out using, first, the time histories of structural responses to stationary excitations, with a large number of samples, satisfying the hypothesis on the input imposed by DD-SSI.

Models of human habits in smart spaces can be expressed by using a multitude of representations whose readability influences the possibility of being validated by human experts. Our research is focused on developing a visual analysis pipeline (service) that allows, starting from the sensor log of a smart space, to graphically visualize human habits. The basic assumption is to apply techniques borrowed from the area of business process automation and mining on a version of the sensor log preprocessed in order to translate raw sensor measurements into human actions.

Human computer interface (HCI) has become more and more important in the last few years. This is mainly due to the increase in the technology and in the new possibilities in yielding a help to disabled people. Brain Computer Interfaces (BCI) represent a subset of the HCI systems which use measurements of the voluntary brain activity for driving a communication system mainly useful for severely disabled people. Electroencephalography (EEG) has been intensively used for the measurement of electrical signals related to the brain activity.

We propose a novel algorithmic framework for the asynchronous and distributed optimization of multi-agent systems. We consider the constrained minimization of a nonconvex and nonsmooth partially separable sum-utility function, i.e., the cost function of each agent depends on the optimization variables of that agent and of its neighbors. This partitioned setting arises in several applications of practical interest.

We consider an anisotropic hyperbolic equation with memory term: ?t2u(x,t)=?i,j=1n?i(aij(x)?ju)+?0t?|?|?2b?(x,t,?)?x?u(x,?)d?+R(x,t)f(x) for $x \in \Omega$ and $t\in (0, T)$ , which is a simplified model equation for viscoelasticity. The main result is a both-sided Lipschitz stability estimate for an inverse source problem of determining a spatial varying factor $f(x)$ of the force term $R(x, t)\,f(x)$ .

Shielding and absorbing screens made of tunable graphene/ dielectric laminate (GL) doped by an electrostatic field bias are designed applying simple modelling procedures in the low-gigahertz frequency range. The adaptive response of both types of screens is achieved through the control of the effective sheet resistance of the GL, consisting of a proper number of doped graphene layers separated by thin films of polyethylene terephthalate (PET).

This paper deals with the application of the recently developed artificial material single layer method to efficiently model a thin conductive anisotropic material using commercial software tools based on the finite element method. In the present work the method is applied to the prediction of the magnetic field in an electric vehicle made with metal or carbon fiber reinforced polymer (CFRP) bodyshell and equipped with a stationary wireless power transfer system. Simple tests are presented to show the performance of the method.