Impact of heterogeneous link qualities and network connectivity on binary consensus

In this paper we consider a network that is trying to reach consensus over the occurrence of an event while communicating over Additive White Gaussian Noise (AWGN) channels. We characterize the impact of different link qualities and network connectivity on consensus performance by analyzing both the asymptotic and transient behaviors. More specifically, we derive a tight approximation for the second largest eigenvalue of the probability transition matrix. We furthermore characterize the dynamics of each individual node. © 2009 AACC.

Optimization of dynamic properties and link performance for directly-modulated gain-coupled DFB lasers

This paper demonstrates the respective roles that combined gain- and index-coupling play in the dynamic properties and overall link performance of DFB lasers. It is shown that for datacommunication applications, modest gain-coupling enables optimum transmission at 10Gbit/s.

Enhanced 10-Gbit/s link performance for directly Modulated complex-coupled DFB lasers via resonance frequency, damping rate, and chirp

This paper demonstrates the respective roles that combined index- and gain-coupling play in the overall link performance of distributed feedback (DFB) lasers. Their impacts on both static and dynamic properties such as slope efficiency, resonance frequency, damping rate, and chirp are investigated. Simulation results are compared with experimental data with good agreement. Transmission-oriented optimization is then demonstrated based on a targeted specification. The design tradeoffs are revealed, and it is shown that a modest combination of index- and gain-coupling enables optimum transmission at 10 Gbit/s.

Auxiliary particle implementation of the probability hypothesis density filter

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), we present a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency. Numerical examples are presented for two scenarios, including a challenging nonlinear observation model.