187 research outputs found
Sequential nonideal measurements of quantum oscillators: Statistical characterization with and without environmental coupling
A one-dimensional quantum oscillator is monitored by taking repeated position
measurements. As a first con- tribution, it is shown that, under a quantum
nondemolition measurement scheme applied to a system initially at the ground
state, (i) the observed sequence of measurements (quantum tracks) corresponding
to a single experiment converges to a limit point, and that (ii) the limit
point is random over the ensemble of the experiments, being distributed as a
zero-mean Gaussian random variable with a variance at most equal to the
ground-state variance. As a second contribution, the richer scenario where the
oscillator is coupled with a frozen (i.e., at the ground state) ensemble of
independent quantum oscillators is considered. A sharply different behavior
emerges: under the same measurement scheme, here we observe that the
measurement sequences are essentially divergent. Such a rigorous statistical
analysis of the sequential measurement process might be useful for
characterizing the main quantities that are currently used for inference,
manipulation, and monitoring of many quantum systems. Several interesting
properties of the quantum tracks evolution, as well as of the associated
(quantum) threshold crossing times, are discussed and the dependence upon the
main system parameters (e.g., the choice of the measurement sampling time, the
degree of interaction with the environment, the measurement device accuracy) is
elucidated. At a more fundamental level, it is seen that, as an application of
basic quantum mechanics principles, a sharp difference exists between the
intrinsic randomness unavoidably present in any quantum system, and the
extrinsic randomness arising from the environmental coupling, i.e., the
randomness induced by an external source of disturbance.Comment: pages 16 Figures
Free-Space Antenna Field/Pattern Retrieval in Reverberation Environments
Simple algorithms for retrieving free-space antenna field or directivity
patterns from complex (field) or real (intensity) measurements taken in ideal
reverberation environments are introduced and discussed.Comment: 6 pages, 2 figures, submitted to IEEE Antennas and Wireless
Propagation Letter
DDoS Attacks with Randomized Traffic Innovation: Botnet Identification Challenges and Strategies
Distributed Denial-of-Service (DDoS) attacks are usually launched through the
, an "army" of compromised nodes hidden in the network. Inferential
tools for DDoS mitigation should accordingly enable an early and reliable
discrimination of the normal users from the compromised ones. Unfortunately,
the recent emergence of attacks performed at the application layer has
multiplied the number of possibilities that a botnet can exploit to conceal its
malicious activities. New challenges arise, which cannot be addressed by simply
borrowing the tools that have been successfully applied so far to earlier DDoS
paradigms. In this work, we offer basically three contributions: we
introduce an abstract model for the aforementioned class of attacks, where the
botnet emulates normal traffic by continually learning admissible patterns from
the environment; we devise an inference algorithm that is shown to
provide a consistent (i.e., converging to the true solution as time progresses)
estimate of the botnet possibly hidden in the network; and we verify the
validity of the proposed inferential strategy over network traces.Comment: Submitted for publicatio
Local Tomography of Large Networks under the Low-Observability Regime
This article studies the problem of reconstructing the topology of a network
of interacting agents via observations of the state-evolution of the agents. We
focus on the large-scale network setting with the additional constraint of
observations, where only a small fraction of the agents can be
feasibly observed. The goal is to infer the underlying subnetwork of
interactions and we refer to this problem as . In order to
study the large-scale setting, we adopt a proper stochastic formulation where
the unobserved part of the network is modeled as an Erd\"{o}s-R\'enyi random
graph, while the observable subnetwork is left arbitrary. The main result of
this work is establishing that, under this setting, local tomography is
actually possible with high probability, provided that certain conditions on
the network model are met (such as stability and symmetry of the network
combination matrix). Remarkably, such conclusion is established under the
- , where the cardinality of the observable
subnetwork is fixed, while the size of the overall network scales to infinity.Comment: To appear in IEEE Transactions on Information Theor
The Embedding Capacity of Information Flows Under Renewal Traffic
Given two independent point processes and a certain rule for matching points
between them, what is the fraction of matched points over infinitely long
streams? In many application contexts, e.g., secure networking, a meaningful
matching rule is that of a maximum causal delay, and the problem is related to
embedding a flow of packets in cover traffic such that no traffic analysis can
detect it. We study the best undetectable embedding policy and the
corresponding maximum flow rate ---that we call the embedding capacity--- under
the assumption that the cover traffic can be modeled as arbitrary renewal
processes. We find that computing the embedding capacity requires the inversion
of very structured linear systems that, for a broad range of renewal models
encountered in practice, admits a fully analytical expression in terms of the
renewal function of the processes. Our main theoretical contribution is a
simple closed form of such relationship. This result enables us to explore
properties of the embedding capacity, obtaining closed-form solutions for
selected distribution families and a suite of sufficient conditions on the
capacity ordering. We evaluate our solution on real network traces, which shows
a noticeable match for tight delay constraints. A gap between the predicted and
the actual embedding capacities appears for looser constraints, and further
investigation reveals that it is caused by inaccuracy of the renewal traffic
model rather than of the solution itself.Comment: Sumbitted to IEEE Trans. on Information Theory on March 10, 201
Diffusion-Based Adaptive Distributed Detection: Steady-State Performance in the Slow Adaptation Regime
This work examines the close interplay between cooperation and adaptation for
distributed detection schemes over fully decentralized networks. The combined
attributes of cooperation and adaptation are necessary to enable networks of
detectors to continually learn from streaming data and to continually track
drifts in the state of nature when deciding in favor of one hypothesis or
another. The results in the paper establish a fundamental scaling law for the
steady-state probabilities of miss-detection and false-alarm in the slow
adaptation regime, when the agents interact with each other according to
distributed strategies that employ small constant step-sizes. The latter are
critical to enable continuous adaptation and learning. The work establishes
three key results. First, it is shown that the output of the collaborative
process at each agent has a steady-state distribution. Second, it is shown that
this distribution is asymptotically Gaussian in the slow adaptation regime of
small step-sizes. And third, by carrying out a detailed large deviations
analysis, closed-form expressions are derived for the decaying rates of the
false-alarm and miss-detection probabilities. Interesting insights are gained.
In particular, it is verified that as the step-size decreases, the error
probabilities are driven to zero exponentially fast as functions of ,
and that the error exponents increase linearly in the number of agents. It is
also verified that the scaling laws governing errors of detection and errors of
estimation over networks behave very differently, with the former having an
exponential decay proportional to , while the latter scales linearly
with decay proportional to . It is shown that the cooperative strategy
allows each agent to reach the same detection performance, in terms of
detection error exponents, of a centralized stochastic-gradient solution.Comment: The paper will appear in IEEE Trans. Inf. Theor
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