Modeling, identification, and control of large-scale dynamical systems

Organizers: Simon Haykin, McMaster University; Eric Moulines, Ecole Nationale Supérieure des Télécommunications; Alfred O. Hero III, University of Michigan

Dynamical systems play an important role in the study of statistical signal processing, and when it is viewed in the context of large-scale systems, the subject matter becomes all the more prevalent and challenging. Examples of large-scale dynamical systems that we intend to discuss in this session include:

• Neurobiological systems
• Large-scale sensor networks
• Wireless communications
• Radar systems

Indeed, it is our strong belief that the study of large-scale dynamical systems is among a very select set of research topics that will occupy the attention of signal processing researchers for many years to come.

Overview lecture:
  Authors: Simon Haykin, Alfred Hero, and Eric Moulines
  Title: Modeling, identification and control of large-scale dynamical systems

Regular lectures:

• Title: On the use of Sequential Monte Carlo to Approximate the Optimal Filter Sensitivity for Parameter Estimation
  Authors: George Poyiadjis, Arnaud Doucet and Sumeetpal S. Singh, Cambridge University
• Title: Adaptive Controlled Brownian Dynamical Systems Approach for the Permeation Problem in Ion Channels
  Authors: Vikram Krishnamurthy, University of British Columbia and Shin-Ho Chung, Australian National University
• Title: Batch and Sequential Bayesian Estimators of the Number of Active Terminals in an IEEE 802.11 Network
  Author: Xiaodong Wang, Columbia University
• Title: Identifying Causal Relationships in large-scale Wireless Sensor/Actuator Networks
  Author: Mark Coates, McGill University
• Title:Tracking Multiple Closely Spaced Targets using an Adaptive Foveal Sensor
  Authors: Fengjun Xi and Darryl Morrell