18,457 research outputs found
An initial approach to distributed adaptive fault-handling in networked systems
We present a distributed adaptive fault-handling algorithm applied in networked systems. The probabilistic approach that we use makes the proposed method capable of adaptively detect and localize network faults by the use of simple end-to-end test transactions. Our method operates in a fully distributed manner, such that each network element detects faults using locally extracted information as input. This allows for a fast autonomous adaption to local network conditions in real-time, with significantly reduced need for manual configuration of algorithm parameters. Initial results from a small synthetically generated network indicate that satisfactory algorithm performance can be achieved, with respect to the number of detected and localized faults, detection time and false alarm rate
Locally adaptive estimation methods with application to univariate time series
The paper offers a unified approach to the study of three locally adaptive
estimation methods in the context of univariate time series from both
theoretical and empirical points of view. A general procedure for the
computation of critical values is given. The underlying model encompasses all
distributions from the exponential family providing for great flexibility. The
procedures are applied to simulated and real financial data distributed
according to the Gaussian, volatility, Poisson, exponential and Bernoulli
models. Numerical results exhibit a very reasonable performance of the methods.Comment: Submitted to the Electronic Journal of Statistics
(http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics
(http://www.imstat.org
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
A Few Photons Among Many: Unmixing Signal and Noise for Photon-Efficient Active Imaging
Conventional LIDAR systems require hundreds or thousands of photon detections
to form accurate depth and reflectivity images. Recent photon-efficient
computational imaging methods are remarkably effective with only 1.0 to 3.0
detected photons per pixel, but they are not demonstrated at
signal-to-background ratio (SBR) below 1.0 because their imaging accuracies
degrade significantly in the presence of high background noise. We introduce a
new approach to depth and reflectivity estimation that focuses on unmixing
contributions from signal and noise sources. At each pixel in an image,
short-duration range gates are adaptively determined and applied to remove
detections likely to be due to noise. For pixels with too few detections to
perform this censoring accurately, we borrow data from neighboring pixels to
improve depth estimates, where the neighborhood formation is also adaptive to
scene content. Algorithm performance is demonstrated on experimental data at
varying levels of noise. Results show improved performance of both reflectivity
and depth estimates over state-of-the-art methods, especially at low
signal-to-background ratios. In particular, accurate imaging is demonstrated
with SBR as low as 0.04. This validation of a photon-efficient, noise-tolerant
method demonstrates the viability of rapid, long-range, and low-power LIDAR
imaging
Maximal adaptive-decision speedups in quantum-state readout
The average time required for high-fidelity readout of quantum states can
be significantly reduced via a real-time adaptive decision rule. An adaptive
decision rule stops the readout as soon as a desired level of confidence has
been achieved, as opposed to setting a fixed readout time . The
performance of the adaptive decision is characterized by the "adaptive-decision
speedup," . In this work, we reformulate this readout problem in terms
of the first-passage time of a particle undergoing stochastic motion. This
formalism allows us to theoretically establish the maximum achievable
adaptive-decision speedups for several physical two-state readout
implementations. We show that for two common readout schemes (the Gaussian
latching readout and a readout relying on state-dependent decay), the speedup
is bounded by and , respectively, in the limit of high single-shot
readout fidelity. We experimentally study the achievable speedup in a
real-world scenario by applying the adaptive decision rule to a readout of the
nitrogen-vacancy-center (NV-center) charge state. We find a speedup of with our experimental parameters. In addition, we propose a simple readout
scheme for which the speedup can, in principle, be increased without bound as
the fidelity is increased. Our results should lead to immediate improvements in
nanoscale magnetometry based on spin-to-charge conversion of the NV-center
spin, and provide a theoretical framework for further optimization of the
bandwidth of quantum measurements.Comment: 18 pages, 11 figures. This version is close to the published versio
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