We introduce novel algorithms for generating random solutions from a uniform distribution over the solutions of a boolean satisfiability problem. Our algorithms operate in two pha...
Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with...
Compressive sampling (CS), or “Compressed Sensing,” has recently generated a tremendous amount of excitement in the image processing community. CS involves taking a relatively...
In this paper we present three algorithms that build graph layouts for undirected, weighted graphs. Our goal is to generate layouts that are consistent with the weights in the gra...
— One of the fundamental problems of the mobile robots is self-localization, i.e. to estimate the self-position by comparing sensor data and a map. In non-stationary environments...
Kanji Tanaka, Tsutomu Hasegawa, Hongbin Zha, Eiji ...