Radial stability of a family of anisotropic Hernquist models with and without a supermassive black hole

We present a method to investigate the radial stability of a spherical anisotropic system that hosts a central supermassive black hole (SBH). Such systems have never been tested before for stability, although high anisotropies have been considered in the dynamical models that were used to estimate the masses of the central putative supermassive black holes. A family of analytical anisotropic spherical Hernquist models with and without a black hole were investigated by means of N-body simulations. A clear trend emerges that the supermassive black hole has a significant effect on the overall stability of the system, i.e. an SBH with a mass of a few percent of the total mass of the galaxy can prevent or reduce the bar instabilities in anisotropic systems. Its mass not only determines the strength of the instability reduction, but also the time in which this occurs. These effects are most significant for models with strong radial anisotropies. Furthermore, our analysis shows that unstable systems with similar SBH but with different anisotropy radii evolve differently: highly radial systems become oblate, while more isotropic models tend to form into prolate structures. In addition to this study, we also present a Monte-Carlo algorithm to generate particles in spherical anisotropic systems.Comment: 16 pages, 12 figures, accepted for publication in MNRAS (some figures have a lowered resolution

A single-chip CMOS pulse oximeter with on-chip lock-in detection

Pulse oximetry is a noninvasive and continuous method for monitoring the blood oxygen saturation level. This paper presents the design and testing of a single-chip pulse oximeter fabricated in a 0.35 µm CMOS process. The chip includes photodiode, transimpedance amplifier, analogue band-pass filters, analogue-to-digital converters, digital signal processor and LED timing control. The experimentally measured AC and DC characteristics of individual circuits including the DC output voltage of the transimpedance amplifier, transimpedance gain of the transimpedance amplifier, and the central frequency and bandwidth of the analogue band-pass filters, show a good match (within 1%) with the circuit simulations. With modulated light source and integrated lock-in detection the sensor effectively suppresses the interference from ambient light and 1/f noise. In a breath hold and release experiment the single chip sensor demonstrates consistent and comparable performance to commercial pulse oximetry devices with a mean of 1.2% difference. The single-chip sensor enables a compact and robust design solution that offers a route towards wearable devices for health monitorin

A critical reassessment of particle Dark Matter limits from dwarf satellites

Dwarf satellite galaxies are ideal laboratories for identifying particle Dark Matter signals. When setting limits on particle Dark Matter properties from null searches, it becomes however crucial the level at which the Dark Matter density profile within these systems is constrained by observations. In the limit in which the spherical Jeans equation is assumed to be valid for a given tracer stellar population, we study the solution of this equation having the Dark Matter mass profile as an output rather than as a trial parametric input. Within our new formulation, we address to what level dwarf spheroidal galaxies feature a reliable mass estimator. We assess then possible extrapolation of the density profiles in the inner regions and -- keeping explicit the dependence on the orbital anisotropy profile of the tracer population -- we derive general trends on the line-of-sight integral of the density profile squared, a quantity commonly dubbed J-factor and crucial to estimate fluxes from prompt Dark Matter pair annihilations. Taking Ursa Minor as a study case among Milky Way satellites, we perform Bayesian inference using the available kinematical data for this galaxy. Contrary to all previous studies, we avoid marginalization over quantities poorly constrained by observations or by theoretical arguments. We find minimal J-factors to be about 2 to 4 times smaller than commonly quoted estimates, approximately relaxing by the same amount the limit on Dark Matter pair annihilation cross section from gamma-ray surveys of Ursa Minor. At the same time, if one goes back to a fixed trial parametric form for the density, e.g. using a NFW or Burkert profile, we show that the minimal J can hardly be reduced by more than a factor of 1.5. \ua9 2016 IOP Publishing Ltd and Sissa Medialab srl

Spike pattern recognition by supervised classification in low dimensional embedding space

© The Author(s) 2016. This article is published with open access at Springerlink.com under the terms of the Creative Commons Attribution License 4.0, (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.Peer reviewedFinal Published versio

A New Method for Detection and Source Analysis of EEG Spikes

Abstract—In the past our research group has developed a method for the detection of focal epileptic EEG (electroencephalogram) spikes that is based on the dipole source localization technique and provides a source localization for each detected spike. In this paper we revisit this method and propose a more accurate explanation of its behavior. Based on this we (i) propose a new method for the detection of epileptic EEG spikes in which the eccentricity of the fitted dipole serves as a new decision variable (ii) conclude that for EEG spike detection one has to make a distinction between EEGs acquired during sleep and during wake. Keywords—Detection, dipole eccentricity, EEG, source analysis, spikes I

Influence of anisotropic conductivities in EEG source estimation in patients with epilepsy

status: publishe