This paper reports work done to improve the modeling of complex processes when only small experimental data sets are available. Neural networks are used to capture the nonlinear un...
W. D. Wan Rosli, Z. Zainuddin, R. Lanouette, S. Sa...
We consider the problem of learning density mixture models for classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent t...
We present Virtual Cord Protocol (VCP), a virtual relative position based routing protocol for sensor networks that also provides methods for data management as known from standar...
The paper presents a framework for semi-supervised nonlinear embedding methods useful for exploratory analysis and visualization of spatio-temporal network data. The method provid...
Disruption-Tolerant Networks (DTNs) deliver data in network environments composed of intermittently connected nodes. Just as in traditional networks, malicious nodes within a DTN ...
John Burgess, George Dean Bissias, Mark D. Corner,...