Search results for "machine"
showing 10 items of 2592 documents
Nonstochastic languages as projections of 2-tape quasideterministic languages
1998
A language L (n) of n-tuples of words which is recognized by a n-tape rational finite-probabilistic automaton with probability 1-e, for arbitrary e > 0, is called quasideterministic. It is proved in [Fr 81], that each rational stochastic language is a projection of a quasideterministic language L (n) of n-tuples of words. Had projections of quasideterministic languages on one tape always been rational stochastic languages, we would have a good characterization of the class of the rational stochastic languages. However we prove the opposite in this paper. A two-tape quasideterministic language exists, the projection of which on the first tape is a nonstochastic language.
Algebraic and logical characterizations of deterministic linear time classes
1997
In this paper an algebraic characterization of the class DLIN of functions that can be computed in linear time by a deterministic RAM using only numbers of linear size is given. This class was introduced by Grandjean, who showed that it is robust and contains most computational problems that are usually considered to be solvable in deterministic linear time.
Algebraic Results on Quantum Automata
2004
We use tools from the algebraic theory of automata to investigate the class of languages recognized by two models of Quantum Finite Automata (QFA): Brodsky and Pippenger’s end-decisive model, and a new QFA model whose definition is motivated by implementations of quantum computers using nucleo-magnetic resonance (NMR). In particular, we are interested in the new model since nucleo-magnetic resonance was used to construct the most powerful physical quantum machine to date. We give a complete characterization of the languages recognized by the new model and by Boolean combinations of the Brodsky-Pippenger model. Our results show a striking similarity in the class of languages recognized by th…
Learning from good examples
1995
The usual information in inductive inference for the purposes of learning an unknown recursive function f is the set of all input /output examples (n,f(n)), n ∈ ℕ. In contrast to this approach we show that it is considerably more powerful to work with finite sets of “good” examples even when these good examples are required to be effectively computable. The influence of the underlying numberings, with respect to which the learning problem has to be solved, to the capabilities of inference from good examples is also investigated. It turns out that nonstandard numberings can be much more powerful than Godel numberings.
Hartmanis-Stearns Conjecture on Real Time and Transcendence
2012
Hartmanis-Stearns conjecture asserts that any number whose decimal expansion can be computed by a multitape Turing machine is either rational or transcendental. After half a century of active research by computer scientists and mathematicians the problem is still open but much more interesting than in 1965.
Electromechanical Numerical Analysis of an Air-Core Pulsed Alternator via Equivalent Network Formulation
2017
In this paper, the numerical analysis on an air-core pulsed alternator is presented. Since compulsators are characterized by very fast electromechanical transients, their accurate analysis requires strong coupling between the equations governing the electrical and the mechanical behaviors. The device is investigated by using a dedicated numerical code capable to take into account eddy currents, compensating windings, as well as the excitation/control circuits. Furthermore, the code is capable of modeling centrifugal forces and vibrations acting on the shaft due to electric and mechanical unbalances or to misalignments of the shaft from its centered position. This makes the code a very power…
Multi-sensor Fusion through Adaptive Bayesian Networks
2011
Common sensory devices for measuring environmental data are typically heterogeneous, and present strict energy constraints; moreover, they are likely affected by noise, and their behavior may vary across time. Bayesian Networks constitute a suitable tool for pre-processing such data before performing more refined artificial reasoning; the approach proposed here aims at obtaining the best trade-off between performance and cost, by adapting the operating mode of the underlying sensory devices. Moreover, self-configuration of the nodes providing the evidence to the Bayesian network is carried out by means of an on-line multi-objective optimization.
Motion sensors for activity recognition in an ambient-intelligence scenario
2013
In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is…
How self-assembly of amphiphilic molecules can generate complexity in the nanoscale
2015
Abstract Given the importance of nanomaterials and nanostructures in modern technology, in the past decades much effort has been directed to set up efficient bottom up protocols for the piloted self-assembly of molecules. However, molecules are generally disinclined to adopt the desired structural organization because they behave according to their own specific intermolecular interactions. Thus, only some selected classes of chemical compounds are capable to lead to useful self-assembled structures. Amphiphiles, simultaneously possessing polar and apolar moieties within their molecular architecture, can give a wide scenario of possible intermolecular interactions: polar–polar, polar–apolar,…
Affinity Sensors for the Diagnosis of COVID-19
2021
The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was proclaimed a global pandemic in March 2020. Reducing the dissemination rate, in particular by tracking the infected people and their contacts, is the main instrument against infection spreading. Therefore, the creation and implementation of fast, reliable and responsive methods suitable for the diagnosis of COVID-19 are required. These needs can be fulfilled using affinity sensors, which differ in applied detection methods and markers that are generating analytical signals. Recently, nucleic acid hybridization, antigen-antibody interaction, and change of reactive oxyge…