Drift Correction Methods for gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges

In this chapter the authors introduce the main challenges faced when developing drift correction techniques and will propose a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques and focusing on challenges still open and waiting for solution

An Application of Reversible Entropic Dynamics on Curved Statistical Manifolds

Entropic Dynamics (ED) is a theoretical framework developed to investigate the possibility that laws of physics reflect laws of inference rather than laws of nature. In this work, a RED (Reversible Entropic Dynamics) model is considered. The geometric structure underlying the curved statistical manifold, M is studied. The trajectories of this particular model are hyperbolic curves (geodesics) on M. Finally, some analysis concerning the stability of these geodesics on M is carried out.Comment: Presented at MaxEnt 2006, the 26th International Workshop on Bayesian Inference and Maximum Entropy Methods (July 8-13, 2006, Paris, France). This paper is slightly updated from the published version. This paper consists of 9 pages with 1 figure. Keywords: Inductive inference, information geometry, statistical manifolds, relative entrop

Dry mergers and the formation of early-type galaxies: constraints from lensing and dynamics

Dissipationless (gas-free or "dry") mergers have been suggested to play a major role in the formation and evolution of early-type galaxies, particularly in growing their mass and size without altering their stellar populations. We perform a new test of the dry merger hypothesis by comparing N-body simulations of realistic systems to empirical constraints provided by recent studies of lens early-type galaxies. We find that major and minor dry mergers: i) preserve the nearly isothermal structure of early-type galaxies within the observed scatter; ii) do not change more than the observed scatter the ratio between total mass M and "virial" mass R_e*sigma/2G (where R_e is the half-light radius and sigma the projected velocity dispersion); iii) increase strongly galaxy sizes [as M^(0.85+/-0.17)] and weakly velocity dispersions [as M^(0.06+/-0.08)] with mass, thus moving galaxies away from the local observed M-R_e and M-sigma relations; iv) introduce substantial scatter in the M-R_e and M-sigma relations. Our findings imply that, unless there is a high degree of fine tuning of the mix of progenitors and types of interactions, present-day massive early-type galaxies cannot have assembled more than ~50% of their mass, and increased their size by more than a factor ~1.8, via dry merging.Comment: ApJ, accepted. 16 pages, 11 figure

Chimera states in heterogeneous networks

Chimera states in networks of coupled oscillators occur when some fraction of the oscillators synchronise with one another, while the remaining oscillators are incoherent. Several groups have studied chimerae in networks of identical oscillators, but here we study these states in a heterogeneous model for which the natural frequencies of the oscillators are chosen from a distribution. We obtain exact results by reduction to a finite set of differential equations. We find that heterogeneity can destroy chimerae, destroy all states except chimerae, or destabilise chimerae in Hopf bifurcations, depending on the form of the heterogeneity.Comment: Revised text. To appear, Chao

A guaranteed-convergence framework for passivity enforcement of linear macromodels

Passivity enforcement is a key step in the extraction of linear macromodels of electrical interconnects and packages for Signal and Power Integrity applications. Most state-of-the-art techniques for passivity enforcement are based on suboptimal or approximate formulations that do not guarantee convergence. We introduce in this paper a new rigorous framework that casts passivity enforcement as a convex non-smooth optimization problem. Thanks to convexity, we are able to prove convergence to the optimal solution within a finite number of steps. The effectiveness of this approach is demonstrated through various numerical example