2,104 research outputs found
Stability analysis and synthesis of systems subject to norm bounded, bounded rate uncertainties
summary:In this paper we consider a linear system subject to norm bounded, bounded rate time-varying uncertainties. Necessary and sufficient conditions for quadratic stability and stabilizability of such class of uncertain systems are well known in the literature. Quadratic stability guarantees exponential stability in presence of arbitrary time-varying uncertainties; therefore it becomes a conservative approach when, as it is the case considered in this paper, the uncertainties are slowly-varying in time. The first contribution of this paper is a sufficient condition for the exponential stability of the zero input system; such condition, which takes into account the bound on the rate of variation of the uncertainties, results to be a less conservative analysis tool than the quadratic stability approach. Then the analysis result is used to provide an algorithm for the synthesis of a controller guaranteeing closed loop stability of the uncertain forced system
The Harmonic Tunneling Tag: a Dual-Band Approach to Backscattering Communications
International audienceAs an answer to the self-interference problem inherent to RFID systems, this work proposes a dual-band backscat-tering architecture. A dual-band RFID system architecture can bring several advantages to the RFID technology. In fact, a dual-band reader could operate without any self-interference cancellation circuit that currently limits its sensitivity; moreover, its dual-band architecture would be compatible with software defined radio-based cellphones. Dual-band readers will need to talk with low-powered dual-band tags that up-convert, modulate, and backscatter the impinging signal from the fundamental frequency to the 2 nd harmonic without weakening the communication link. This article reports on the UHF dual-band Harmonic Tun-neling Tag capable of backscattering and amplifying a 900 MHz signal when receiving an input carrier at 450 MHz. The Harmonic Tunneling Tag provides positive conversion gains (+7 dB) that improve the communication link by a factor of 2 and operates under a very low bias voltage of 0.16 V
Concentrated suspensions of Brownian beads in water: dynamic heterogeneities trough a simple experimental technique
Concentrated suspensions of Brownian hard-spheres in water are an epitome for
understanding the glassy dynamics of both soft materials and supercooled
molecular liquids. From an experimental point of view, such systems are
especially suited to perform particle tracking easily, and, therefore, are a
benchmark for novel optical techniques, applicable when primary particles
cannot be resolved. Differential Variance Analysis (DVA) is one such novel
technique that simplifies significantly the characterization of structural
relaxation processes of soft glassy materials, since it is directly applicable
to digital image sequences of the sample. DVA succeeds in monitoring not only
the average dynamics, but also its spatio-temporal fluctuations, known as
dynamic heterogeneities. In this work, we study the dynamics of dense
suspensions of Brownian beads in water, imaged through digital
video-microscopy, by using both DVA and single-particle tracking. We focus on
two commonly used signatures of dynamic heterogeneities: the dynamic
susceptibility, , and the non-Gaussian parameter, . By direct
comparison of these two quantities, we are able to highlight similarities and
differences. We do confirm that and provide qualitatively
similar information, but we find quantitative discrepancies in the scalings of
characteristic time and length scale on approaching the glass transition.Comment: The original publication is available at http://www.scichina.com and
http://www.springerlink.com
http://engine.scichina.com/publisher/scp/journal/SCPMA/doi/10.1007/s11433-019-9401-x?slug=abstrac
SAI, a Sensible Artificial Intelligence that plays Go
We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero
paradigm. The winrate as a function of the komi is modeled with a
two-parameters sigmoid function, so that the neural network must predict just
one more variable to assess the winrate for all komi values. A second novel
feature is that training is based on self-play games that occasionally branch
-- with changed komi -- when the position is uneven. With this setting,
reinforcement learning is showed to work on 7x7 Go, obtaining very strong
playing agents. As a useful byproduct, the sigmoid parameters given by the
network allow to estimate the score difference on the board, and to evaluate
how much the game is decided.Comment: Updated for IJCNN 2019 conferenc
Enabling IoT stream management in multi-cloud environment by orchestration
(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Every-Day lives are becoming increasingly instrumented by electronic devices and any kind of computer-based (distributed) service. As a result, organizations need to analyse an enormous amounts of data in order to increase their incomings or to improve their services. Anyway, setting-up a private infrastructure to execute analytics over Big Data is still expensive. The exploitation of Cloud infrastructure in IoT Stream management is appealing because of costs reductions and potentiality of storage, network and computing resources. The Cloud can consistently reduce the cost of analysis of data from different sources, opening analytics to big storages in a multi-cloud environment. Anyway, creating and executing this kind of service is very complex since different resources have to be provisioned and coordinated depending on users' needs. Orchestration is a solution to this problem, but it requires proper languages and methodologies for automatic composition and execution. In this work we propose a methodology for composition of services used for analyses of different IoT Stream and, in general, Big Data sources: in particular an Orchestration language is reported able to describe composite services and resources in a multi-cloud environment.Peer ReviewedPostprint (author's final draft
Generation of game contents by social media analysis and MAS planning
In the age of pervasive computing and social networks, it has become commonplace to retrieve opinions about digital contents in games. In the case of multi-player, open world gaming, in fact even in “old-school” single players games, it is evident the need for adding new features in a game depending on users comments and needs. However this is a challenging task that usually requires considerable design and programming efforts, and more and more patches to games, with the inevitable consequence of loosing interest in the game by players over years. This is particularly a hard problem for all games that do not intend to be designed as interactive novels. Process Content Generation (PCG) of new contents could be a solution to this problem, but usually such techniques are used to design new maps or graphical contents. Here we propose a novel PCG technique able to introduce new contents in games by means of new story-lines and quests. We introduce new intelligent agents and events in the world: their attitudes and behaviors will promote new actions in the game, leading to the involvement of players in new gaming content. The whole methodology is driven by Social Media Analysis contents about the game, and by the use of formal planning techniques based on Multi-Agents modelsPeer ReviewedPostprint (author's final draft
Multiparametric Investigation of Dynamics in Fetal Heart Rate Signals
In the field of electronic fetal health monitoring, computerized analysis of fetal heart rate
(FHR) signals has emerged as a valid decision-support tool in the assessment of fetal wellbeing.
Despite the availability of several approaches to analyze the variability of FHR signals (namely
the FHRV), there are still shadows hindering a comprehensive understanding of how linear and
nonlinear dynamics are involved in the control of the fetal heart rhythm. In this study, we propose
a straightforward processing and modeling route for a deeper understanding of the relationships
between the characteristics of the FHR signal. A multiparametric modeling and investigation of the
factors influencing the FHR accelerations, chosen as major indicator of fetal wellbeing, is carried out
by means of linear and nonlinear techniques, blockwise dimension reduction, and artificial neural
networks. The obtained results show that linear features are more influential compared to nonlinear
ones in the modeling of HRV in healthy fetuses. In addition, the results suggest that the investigation
of nonlinear dynamics and the use of predictive tools in the field of FHRV should be undertaken
carefully and limited to defined pregnancy periods and FHR mean values to provide interpretable
and reliable information to clinicians and researchers
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