31,886 research outputs found
Automatic Mode Switching in Atrial Fibrillation
Automatic mode switching (AMS) algorithms were designed to prevent tracking of atrial tachyarrhythmias (ATA) or other rapidly occurring signals sensed by atrial channels, thereby reducing the adverse hemodynamic and symptomatic consequences of a rapid ventricular response. The inclusion of an AMS function in most dual chamber pacemaker now provides optimal management of atrial arrhythmias and allows the benefit of atrioventricular synchrony to be extended to a population with existing atrial fibrillation. Appropriate AMS depends on several parameters: a) the programmed parameters; b) the characteristics of the arrhythmia; c) the characteristics of the AMS algorithm. Three qualifying aspects constitute an AMS algorithm: onset, AMS response, and resynchronization. Since AMS programs also provide data on the time of onset and duration of AMS episodes, AMS data may be interpreted as a surrogate marker of ATAs recurrence. Recently, stored electrograms corresponding to episodes of ATAs have been introduced, thus clarifying the accuracy of AMS in detecting ATAs Clinically this information may be used to assess the efficacy of an antiarrhythmic intervention or the risk of thromboembolic events, and it may serve as a valuable research tool for evaluating the natural history and burden of ATAs
Shortest, Fastest, and Foremost Broadcast in Dynamic Networks
Highly dynamic networks rarely offer end-to-end connectivity at a given time.
Yet, connectivity in these networks can be established over time and space,
based on temporal analogues of multi-hop paths (also called {\em journeys}).
Attempting to optimize the selection of the journeys in these networks
naturally leads to the study of three cases: shortest (minimum hop), fastest
(minimum duration), and foremost (earliest arrival) journeys. Efficient
centralized algorithms exists to compute all cases, when the full knowledge of
the network evolution is given.
In this paper, we study the {\em distributed} counterparts of these problems,
i.e. shortest, fastest, and foremost broadcast with termination detection
(TDB), with minimal knowledge on the topology.
We show that the feasibility of each of these problems requires distinct
features on the evolution, through identifying three classes of dynamic graphs
wherein the problems become gradually feasible: graphs in which the
re-appearance of edges is {\em recurrent} (class R), {\em bounded-recurrent}
(B), or {\em periodic} (P), together with specific knowledge that are
respectively (the number of nodes), (a bound on the recurrence
time), and (the period). In these classes it is not required that all pairs
of nodes get in contact -- only that the overall {\em footprint} of the graph
is connected over time.
Our results, together with the strict inclusion between , , and ,
implies a feasibility order among the three variants of the problem, i.e.
TDB[foremost] requires weaker assumptions on the topology dynamics than
TDB[shortest], which itself requires less than TDB[fastest]. Reversely, these
differences in feasibility imply that the computational powers of ,
, and also form a strict hierarchy
Termination Detection of Local Computations
Contrary to the sequential world, the processes involved in a distributed
system do not necessarily know when a computation is globally finished. This
paper investigates the problem of the detection of the termination of local
computations. We define four types of termination detection: no detection,
detection of the local termination, detection by a distributed observer,
detection of the global termination. We give a complete characterisation
(except in the local termination detection case where a partial one is given)
for each of this termination detection and show that they define a strict
hierarchy. These results emphasise the difference between computability of a
distributed task and termination detection. Furthermore, these
characterisations encompass all standard criteria that are usually formulated :
topological restriction (tree, rings, or triangu- lated networks ...),
topological knowledge (size, diameter ...), and local knowledge to distinguish
nodes (identities, sense of direction). These results are now presented as
corollaries of generalising theorems. As a very special and important case, the
techniques are also applied to the election problem. Though given in the model
of local computations, these results can give qualitative insight for similar
results in other standard models. The necessary conditions involve graphs
covering and quasi-covering; the sufficient conditions (constructive local
computations) are based upon an enumeration algorithm of Mazurkiewicz and a
stable properties detection algorithm of Szymanski, Shi and Prywes
Fast Computation of Small Cuts via Cycle Space Sampling
We describe a new sampling-based method to determine cuts in an undirected
graph. For a graph (V, E), its cycle space is the family of all subsets of E
that have even degree at each vertex. We prove that with high probability,
sampling the cycle space identifies the cuts of a graph. This leads to simple
new linear-time sequential algorithms for finding all cut edges and cut pairs
(a set of 2 edges that form a cut) of a graph.
In the model of distributed computing in a graph G=(V, E) with O(log V)-bit
messages, our approach yields faster algorithms for several problems. The
diameter of G is denoted by Diam, and the maximum degree by Delta. We obtain
simple O(Diam)-time distributed algorithms to find all cut edges,
2-edge-connected components, and cut pairs, matching or improving upon previous
time bounds. Under natural conditions these new algorithms are universally
optimal --- i.e. a Omega(Diam)-time lower bound holds on every graph. We obtain
a O(Diam+Delta/log V)-time distributed algorithm for finding cut vertices; this
is faster than the best previous algorithm when Delta, Diam = O(sqrt(V)). A
simple extension of our work yields the first distributed algorithm with
sub-linear time for 3-edge-connected components. The basic distributed
algorithms are Monte Carlo, but they can be made Las Vegas without increasing
the asymptotic complexity.
In the model of parallel computing on the EREW PRAM our approach yields a
simple algorithm with optimal time complexity O(log V) for finding cut pairs
and 3-edge-connected components.Comment: Previous version appeared in Proc. 35th ICALP, pages 145--160, 200
Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling
Measurement samples are often taken in various monitoring applications. To
reduce the sensing cost, it is desirable to achieve better sensing quality
while using fewer samples. Compressive Sensing (CS) technique finds its role
when the signal to be sampled meets certain sparsity requirements. In this
paper we investigate the possibility and basic techniques that could further
reduce the number of samples involved in conventional CS theory by exploiting
learning-based non-uniform adaptive sampling.
Based on a typical signal sensing application, we illustrate and evaluate the
performance of two of our algorithms, Individual Chasing and Centroid Chasing,
for signals of different distribution features. Our proposed learning-based
adaptive sampling schemes complement existing efforts in CS fields and do not
depend on any specific signal reconstruction technique. Compared to
conventional sparse sampling methods, the simulation results demonstrate that
our algorithms allow less number of samples for accurate signal
reconstruction and achieve up to smaller signal reconstruction error
under the same noise condition.Comment: 9 pages, IEEE MASS 201
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