416 research outputs found
Avoiding Aliasing in Allan Variance: an Application to Fiber Link Data Analysis
Optical fiber links are known as the most performing tools to transfer
ultrastable frequency reference signals. However, these signals are affected by
phase noise up to bandwidths of several kilohertz and a careful data processing
strategy is required to properly estimate the uncertainty. This aspect is often
overlooked and a number of approaches have been proposed to implicitly deal
with it. Here, we face this issue in terms of aliasing and show how typical
tools of signal analysis can be adapted to the evaluation of optical fiber
links performance. In this way, it is possible to use the Allan variance as
estimator of stability and there is no need to introduce other estimators. The
general rules we derive can be extended to all optical links. As an example, we
apply this method to the experimental data we obtained on a 1284 km coherent
optical link for frequency dissemination, which we realized in Italy
Detection of ultra-high resonance contrast in vapor cell atomic clocks
We propose and demonstrate a novel detection scheme of clock signals and
obtain an ultra-high resonance contrast above 90%. The precision of the
signal's detection and the signal-to-noise ratio (SNR) of atomic clock signal
is improved remarkably. The frequency stability in terms of Allan deviation has
been improved by an order for the new detection under the equivalent
conditions. We also investigate density effect which produces the splitting of
the transmission peak and consequently a narrower linewidth of Ramsey fringes.Comment: 5 pages, 4 figure
Cooperative Monitoring to Diagnose Multiagent Plans
Diagnosing the execution of a Multiagent Plan (MAP) means identifying and explaining action failures (i.e., actions that did not reach their expected effects). Current approaches to MAP diagnosis are substantially centralized, and assume that action failures are inde-pendent of each other. In this paper, the diagnosis of MAPs, executed in a dynamic and partially observable environment, is addressed in a fully distributed and asynchronous way; in addition, action failures are no longer assumed as independent of each other. The paper presents a novel methodology, named Cooperative Weak-Committed Moni-toring (CWCM), enabling agents to cooperate while monitoring their own actions. Coop-eration helps the agents to cope with very scarcely observable environments: what an agent cannot observe directly can be acquired from other agents. CWCM exploits nondetermin-istic action models to carry out two main tasks: detecting action failures and building trajectory-sets (i.e., structures representing the knowledge an agent has about the environ-ment in the recent past). Relying on trajectory-sets, each agent is able to explain its own action failures in terms of exogenous events that have occurred during the execution of the actions themselves. To cope with dependent failures, CWCM is coupled with a diagnostic engine that distinguishes between primary and secondary action failures. An experimental analysis demonstrates that the CWCM methodology, together with the proposed diagnostic inferences, are effective in identifying and explaining action failures even in scenarios where the system observability is significantly reduced. 1
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