714 research outputs found
Statistical Learning Theory for Location Fingerprinting in Wireless LANs
In this paper, techniques and algorithms developed in the framework of statistical learning theory are analyzed and applied to the problem of determining the location of a wireless device by measuring the signal strengths from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no custom hardware is required. The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in the literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques
Location-aware computing: a neural network model for determining location in wireless LANs
The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented
A model of protocell based on the introduction of a semi-permeable membrane in a stochastic model of catalytic reaction networks
In this work we introduce some preliminary analyses on the role of a
semi-permeable membrane in the dynamics of a stochastic model of catalytic
reaction sets (CRSs) of molecules. The results of the simulations performed on
ensembles of randomly generated reaction schemes highlight remarkable
differences between this very simple protocell description model and the
classical case of the continuous stirred-tank reactor (CSTR). In particular, in
the CSTR case, distinct simulations with the same reaction scheme reach the
same dynamical equilibrium, whereas, in the protocell case, simulations with
identical reaction schemes can reach very different dynamical states, despite
starting from the same initial conditions.Comment: In Proceedings Wivace 2013, arXiv:1309.712
A stochastic model of catalytic reaction networks in protocells
Protocells are supposed to have played a key role in the self-organizing
processes leading to the emergence of life. Existing models either (i) describe
protocell architecture and dynamics, given the existence of sets of
collectively self-replicating molecules for granted, or (ii) describe the
emergence of the aforementioned sets from an ensemble of random molecules in a
simple experimental setting (e.g. a closed system or a steady-state flow
reactor) that does not properly describe a protocell. In this paper we present
a model that goes beyond these limitations by describing the dynamics of sets
of replicating molecules within a lipid vesicle. We adopt the simplest possible
protocell architecture, by considering a semi-permeable membrane that selects
the molecular types that are allowed to enter or exit the protocell and by
assuming that the reactions take place in the aqueous phase in the internal
compartment. As a first approximation, we ignore the protocell growth and
division dynamics. The behavior of catalytic reaction networks is then
simulated by means of a stochastic model that accounts for the creation and the
extinction of species and reactions. While this is not yet an exhaustive
protocell model, it already provides clues regarding some processes that are
relevant for understanding the conditions that can enable a population of
protocells to undergo evolution and selection.Comment: 20 pages, 5 figure
Dynamical criticality: overview and open questions
Systems that exhibit complex behaviours are often found in a particular dynamical condition, poised between order and disorder. This observation is at the core of the so-called criticality hypothesis, which states that systems in a dynamical regime between order and disorder attain the highest level of computational capabilities and achieve an optimal trade-off between robustness and flexibility. Recent results in cellular and evolutionary biology, neuroscience and computer science have revitalised the interest in the criticality hypothesis, emphasising its role as a viable candidate general law in adaptive complex systems. This paper provides an overview of the works on dynamical criticality that are - to the best of our knowledge - particularly relevant for the criticality hypothesis. The authors review the main contributions concerning dynamics and information processing at the edge of chaos, and illustrate the main achievements in the study of critical dynamics in biological systems. Finally, the authors discuss open questions and propose an agenda for future work
Choice of three different intramedullary nails in the treatment of trochanteric fractures: outcome, analysis and consideration in midterm
paragone di 3 differenti impianti per frattura pertrocanterica femoreThe purpose of this study is to compare the results obtained using three different systems of osteosynthesis, developed for the surgical treatment of fractures of the trochanteric region of the femur, based on the principle intramedullary nailing: the Gamma nail, the Affixus nail and the ZNN nail. This is a retrospective study: 72 trochanteric fractures treated with the Gamma nail, 68 treated with the Affixus nail and 69 treated with the ZNN nail, between the years 2012 and 2014, with the prerequisite of a minimum follow-up of 18 months. The fractures were classified according to the AO system; the most commonly reported subtype was the A2 fracture. Clinical and radiographic examinations were performed, both at hospital admission and post-operatively, at 1, 3, 6, 12 and 18 months. Of the 209 patients, 171 were women and 38 were men. The average age was 83.12 years old. All three systems guaranteed an early mobilization and ambulation in most of the patients. There were no significant differences in the use of the three nails in terms of recovery of previous functional capacity, or in terms of the time required for the fracture to heal. There were no advantages encountered with the use of one intramedullary nail over another and, in particular, when observing the complications and patient outcome, there were no statistically significant differences detected
The role of backward reactions in a stochastic model of catalytic reaction networks
We investigate the role of backward reactions in a stochastic model of catalytic reaction network, with specific regard to the influence on the emergence of autocatalytic sets (ACSs), which are supposed to be one of the pre-requisites in the transition between non-living to living matter.
In particular, we analyse the impact that a variation in the kinetic rates of forward and backward reactions may have on the overall dynamics.
Significant effects are indeed observed, provided that the intensity of backward reactions is sufficiently high. In spite of an invariant activity of the system in terms of production of new species, as backward reactions are intensified, the emergence of ACSs becomes more likely and an increase in their number, as well as in the proportion of species belonging to them, is observed. Furthermore, ACSs appear to be more robust to fluctuations than in the usual settings with no backward reaction.
This outcome may rely not only on the higher average connectivity of the reaction graph, but also on the distinguishing property of backward reactions of recreating the substrates of the corresponding forward reactions
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