@conference {
	title = {Solving Optimal Neural Layout by Gibbs Sampling},
	author = {Jiang, Tao., Xu, Peng., Abshire, Pamela A., Baras, John S.},
	year = {2005},
	month = {07/2005},
	abstract = {Neural systems of organisms derive their functionality largely from the numerous and intricate connections between individual components. These connections are costly and have been refined via evolutionary pressure that acts to maximize their functionality while minimizing the associated cost. This tradeoff can be formulated as a constrained optimization problem. In this paper, we use simulated annealing, implemented through Gibbs sampling, to investigate the minimal cost placement of individual components in neural systems. We show that given the constraints and the presumed cost function associated with the neural interconnections, we can find the configuration corresponding to the minimal cost. We restrict the mechanisms considered to those involving incremental improvement through local interactions since real neural systems are likely to be subject to such constraints. By adjusting the cost function and comparing with the actual configuration in neural systems, we can infer the actual cost function associated with the connections used by nature. This provides a powerful tool to biologists for investigating the configurations of neural systems.},
	address = {Salt Lake City, Utah},
	booktitle = {8th Joint Conference on Information Sciences 2005 (JCIS 2005 in conjunction with the 7th International Conference on Computational Intelligence and Natural Computation 2005, CINC)},
	pages = {pp. 424-427}
}
@conference {
	title = {Wireless Sensor Networks and Applications},
	author = {Baras, John S.},
	year = {2005},
	month = {07/2005},
	address = {Athens, Greece},
	organization = {Athens Agricultural University}
}
@conference {
	title = {Distributed Stochastic Coordination Algorithms for Autonomous Vehicle Control},
	author = {Baras, John S.},
	year = {2005},
	month = {07/2005},
	address = {New Orleans},
	booktitle = {Cooperative Control Mini-Symposium, 2005 SIAM Conference on Control Theory and Applications}
}
@conference {
	title = {Gibbs Sampler-Based Path Planning for Autonomous Vehicles: Convergence analysis},
	author = {Xi, Wei., Tan, Xiaobo., Baras, John S.},
	year = {2005},
	month = {07/2005},
	abstract = {Simulation has indicated that distributed self-organization of autonomous swarms might be achieved through Gibbs sampler-based simulated annealing. However, the dynamic nature of the underlying graph presents significant challenges in convergence analysis. As a first step toward such analysis, convergence of the algorithm is established in this paper for two special cases: single vehicle with limited sensing/moving range, and multiple vehicles with full sensing/moving range. The impact of Gibbs potential functions on the convergence speed is also investigated, which provides insight into the design of these functions.},
	address = {Prague, Czech Republic},
	booktitle = {IFAC 2005}
}
