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IEEEICCI
2003
IEEE
16 years 22 hour ago
Signal Classification through Multifractal Analysis and Complex Domain Neural Networks
This paper describes a system capable of classifying stochastic, self-affine, nonstationary signals produced by nonlinear systems. The classification and analysis of these signals...
Witold Kinsner, V. Cheung, K. Cannons, J. Pear, T....
AIME
2001
Springer
15 years 11 months ago
NasoNet, Joining Bayesian Networks and Time to Model Nasopharyngeal Cancer Spread
Abstract. Cancer spread is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should...
Severino F. Galán, Francisco Aguado, Franci...
AINA
2003
IEEE
15 years 10 months ago
Formal Verification of Condition Data Flow Diagrams for Assurance of Correct Network Protocols
Condition Data Flow Diagrams (CDFDs) are a formalized notation resulting from the integration of Yourdon Data Flow Diagrams, Petri Nets, and pre-post notation. They are used in th...
Shaoying Liu
CIDR
2007
185views Algorithms» more  CIDR 2007»
15 years 8 months ago
Rethinking Data Management for Storage-centric Sensor Networks
Data management in wireless sensor networks has been an area of significant research in recent years. Many existing sensor data management systems view sensor data as a continuou...
Yanlei Diao, Deepak Ganesan, Gaurav Mathur, Prasha...
UAI
2003
15 years 8 months ago
Learning Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBN) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cycli...
Uri Nodelman, Christian R. Shelton, Daphne Koller