1,694 research outputs found
Driven lattice gas of dimers coupled to a bulk reservoir
We investigate the non-equilibrium steady state of a one-dimensional (1D)
lattice gas of dimers. The dynamics is described by a totally asymmetric
exclusion process (TASEP) supplemented by attachment and detachment processes,
mimicking chemical equilibrium of the 1D driven transport with the bulk
reservoir. The steady-state phase diagram, current and density profiles are
calculated using both a refined mean-field theory and extensive stochastic
simulations. As a consequence of the on-off kinetics, a new phase coexistence
region arises intervening between low and high density phases such that the
discontinuous transition line of the TASEP splits into two continuous ones. The
results of the mean-field theory and simulations are found to coincide. We show
that the physical picture obtained in the corresponding lattice gas model with
monomers is robust, in the sense that the phase diagram changes quantitatively,
but the topology remains unaltered. The mechanism for phase separation is
identified as generic for a wide class of driven 1D lattice gases.Comment: 15 pages, 10 figures, 1tabl
Gaussian states and geometrically uniform symmetry
Quantum Gaussian states can be considered as the majority of the practical
quantum states used in quantum communications and more generally in quantum
information. Here we consider their properties in relation with the
geometrically uniform symmetry, a property of quantum states that greatly
simplifies the derivation of the optimal decision by means of the square root
measurements. In a general framework of the -mode Gaussian states we show
the general properties of this symmetry and the application of the optimal
quantum measurements. An application example is presented, to quantum
communication systems employing pulse position modulation. We prove that the
geometrically uniform symmetry can be applied to the general class of multimode
Gaussian states
Exact Spectral Analysis of Single-h and Multi-h CPM Signals through PAM decomposition and Matrix Series Evaluation
In this paper we address the problem of closed-form spectral evaluation of
CPM. We show that the multi-h CPM signal can be conveniently generated by a PTI
SM. The output is governed by a Markov chain with the unusual peculiarity of
being cyclostationary and reducible; this holds also in the single-h context.
Judicious reinterpretation of the result leads to a formalization through a
stationary and irreducible Markov chain, whose spectral evaluation is known in
closed-form from the literature. Two are the major outcomes of this paper.
First, unlike the literature, we obtain a PSD in true closed-form. Second, we
give novel insights into the CPM format.Comment: 31 pages, 10 figure
Theory of Quantum Pulse Position Modulation and Related Numerical Problems
The paper deals with quantum pulse position modulation (PPM), both in the
absence (pure states) and in the presence (mixed states) of thermal noise,
using the Glauber representation of coherent laser radiation. The objective is
to find optimal (or suboptimal) measurement operators and to evaluate the
corresponding error probability. For PPM, the correct formulation of quantum
states is given by the tensorial product of m identical Hilbert spaces, where m
is the PPM order. The presence of mixed states, due to thermal noise, generates
an optimization problem involving matrices of huge dimensions, which already
for 4-PPM, are of the order of ten thousand. To overcome this computational
complexity, the currently available methods of quantum detection, which are
based on explicit results, convex linear programming and square root
measurement, are compared to find the computationally less expensive one. In
this paper a fundamental role is played by the geometrically uniform symmetry
of the quantum PPM format. The evaluation of error probability confirms the
vast superiority of the quantum detection over its classical counterpart.Comment: 10 pages, 7 figures, accepted for publication in IEEE Trans. on
Communication
Fast and Robust Detection of Fallen People from a Mobile Robot
This paper deals with the problem of detecting fallen people lying on the
floor by means of a mobile robot equipped with a 3D depth sensor. In the
proposed algorithm, inspired by semantic segmentation techniques, the 3D scene
is over-segmented into small patches. Fallen people are then detected by means
of two SVM classifiers: the first one labels each patch, while the second one
captures the spatial relations between them. This novel approach showed to be
robust and fast. Indeed, thanks to the use of small patches, fallen people in
real cluttered scenes with objects side by side are correctly detected.
Moreover, the algorithm can be executed on a mobile robot fitted with a
standard laptop making it possible to exploit the 2D environmental map built by
the robot and the multiple points of view obtained during the robot navigation.
Additionally, this algorithm is robust to illumination changes since it does
not rely on RGB data but on depth data. All the methods have been thoroughly
validated on the IASLAB-RGBD Fallen Person Dataset, which is published online
as a further contribution. It consists of several static and dynamic sequences
with 15 different people and 2 different environments
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