We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
This paper presents an approach to the analysis of task sets implemented on multiprocessor systems, when the task execution times are specified as generalized probability distrib...
In this paper, we present a novel statistical full-chip leakage power analysis method. The new method can provide a general framework to derive the full-chip leakage current or po...
Ruijing Shen, Ning Mi, Sheldon X.-D. Tan, Yici Cai...
A class of nonlinear transformation-based filters (NLTF) for state estimation is proposed. The nonlinear transformations that can be used include first (TT1) and second (TT2) or...