18,758 research outputs found
Shape minimization of the dissipated energy in dyadic trees
In this paper, we study the role of boundary conditions on the optimal shape
of a dyadic tree in which flows a Newtonian fluid. Our optimization problem
consists in finding the shape of the tree that minimizes the viscous energy
dissipated by the fluid with a constrained volume, under the assumption that
the total flow of the fluid is conserved throughout the structure. These
hypotheses model situations where a fluid is transported from a source towards
a 3D domain into which the transport network also spans. Such situations could
be encountered in organs like for instance the lungs and the vascular networks.
Two fluid regimes are studied: (i) low flow regime (Poiseuille) in trees with
an arbitrary number of generations using a matricial approach and (ii) non
linear flow regime (Navier-Stokes, moderate regime with a Reynolds number 100)
in trees of two generations using shape derivatives in an augmented Lagrangian
algorithm coupled with a 2D/3D finite elements code to solve Navier-Stokes
equations. It relies on the study of a finite dimensional optimization problem
in the case (i) and on a standard shape optimization problem in the case (ii).
We show that the behaviours of both regimes are very similar and that the
optimal shape is highly dependent on the boundary conditions of the fluid
applied at the leaves of the tree.Comment: \`a para\^itre dans Discrete Contin. Dyn. Syst. (B
Classification of sporting activities using smartphone accelerometers
In this paper we present a framework that allows for the automatic identification of sporting activities using commonly available smartphones. We extract discriminative informational features from smartphone accelerometers using the Discrete Wavelet Transform (DWT). Despite the poor quality of their accelerometers, smartphones were used as capture devices due to their prevalence in today’s society. Successful classification on this basis potentially makes the technology accessible to both elite and non-elite athletes. Extracted features are used to train different categories of classifiers. No one classifier family has a reportable direct advantage in activity classification problems to date; thus we examine classifiers from each of the most widely used classifier families. We investigate three classification approaches; a commonly used SVM-based approach, an optimized classification model and a fusion of classifiers. We also investigate the effect of changing several of the DWT input parameters, including mother wavelets, window lengths and DWT decomposition levels. During the course of this work we created a challenging
sports activity analysis dataset, comprised of soccer and field-hockey activities. The average maximum F-measure accuracy of 87% was achieved using a fusion of classifiers, which was 6% better than a single classifier model and 23% better than a standard SVM approach
A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networks
The densification of small-cell base stations in a 5G architecture is a
promising approach to enhance the coverage area and facilitate the ever
increasing capacity demand of end users. However, the bottleneck is an
intelligent management of a backhaul/fronthaul network for these small-cell
base stations. This involves efficient association and placement of the
backhaul hubs that connects these small-cells with the core network.
Terrestrial hubs suffer from an inefficient non line of sight link limitations
and unavailability of a proper infrastructure in an urban area. Seeing the
popularity of flying platforms, we employ here an idea of using networked
flying platform (NFP) such as unmanned aerial vehicles (UAVs), drones, unmanned
balloons flying at different altitudes, as aerial backhaul hubs. The
association problem of these NFP-hubs and small-cell base stations is
formulated considering backhaul link and NFP related limitations such as
maximum number of supported links and bandwidth. Then, this paper presents an
efficient and distributed solution of the designed problem, which performs a
greedy search in order to maximize the sum rate of the overall network. A
favorable performance is observed via a numerical comparison of our proposed
method with optimal exhaustive search algorithm in terms of sum rate and
run-time speed.Comment: Submitted to IEEE GLOBECOM 2017, 7 pages and 4 figure
A scalable hardware and software control apparatus for experiments with hybrid quantum systems
Modern experiments with fundamental quantum systems - like ultracold atoms,
trapped ions, single photons - are managed by a control system formed by a
number of input/output electronic channels governed by a computer. In hybrid
quantum systems, where two or more quantum systems are combined and made to
interact, establishing an efficient control system is particularly challenging
due to the higher complexity, especially when each single quantum system is
characterized by a different timescale. Here we present a new control apparatus
specifically designed to efficiently manage hybrid quantum systems. The
apparatus is formed by a network of fast communicating Field Programmable Gate
Arrays (FPGAs), the action of which is administrated by a software. Both
hardware and software share the same tree-like structure, which ensures a full
scalability of the control apparatus. In the hardware, a master board acts on a
number of slave boards, each of which is equipped with an FPGA that locally
drives analog and digital input/output channels and radiofrequency (RF) outputs
up to 400 MHz. The software is designed to be a general platform for managing
both commercial and home-made instruments in a user-friendly and intuitive
Graphical User Interface (GUI). The architecture ensures that complex control
protocols can be carried out, such as performing of concurrent commands loops
by acting on different channels, the generation of multi-variable error
functions and the implementation of self-optimization procedures. Although
designed for managing experiments with hybrid quantum systems, in particular
with atom-ion mixtures, this control apparatus can in principle be used in any
experiment in atomic, molecular, and optical physics.Comment: 10 pages, 12 figure
Accidental Father-to-Son HIV-1 Transmission During the Seroconversion Period
A 4-year-old child born to an HIV-1 seronegative mother was diagnosed with HIV-1, the main risk factor being transmission from the child's father who was seroconverting at the time of the child's birth. In the context of a forensic investigation, we aimed to identify the source of infection of the child and date of the transmission event. Samples were collected from the father and child at two time points about 4 years after the child's birth. Partial segments of three HIV-1 genes (gag, pol, and env) were sequenced and maximum likelihood (ML) and Bayesian methods were used to determine direction and estimate date of transmission. Neutralizing antibodies were determined using a single cycle assay. Bayesian trees displayed a paraphyletic-monophyletic topology in all three genomic regions, with the father's host label at the root, which is consistent with father-to-son transmission. ML trees found similar topologies in gag and pol and a monophyletic-monophyletic topology in env. Analysis of the time of the most recent common ancestor of each HIV-1 gene population indicated that the child was infected shortly after the father. Consistent with the infection history, both father and son developed broad and potent HIV-specific neutralizing antibody responses. In conclusion, the direction of transmission implicated the father as the source of transmission. Transmission occurred during the seroconversion period when the father was unaware of the infection and was likely accidental. This case shows how genetic, phylogenetic, and serological data can contribute for the forensic investigation of HIV transmission.info:eu-repo/semantics/publishedVersio
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