In this paper, we propose a novel multi-dimensional distributed hidden Markov model (DHMM) framework. We first extend the theory of 2D hidden Markov models (HMMs) to arbitrary ca...
This paper reviews a set of techniques for compiling dataflow-based, graphical programs for embedded signal processing applications into efficient implementations on programmable ...
Shuvra S. Bhattacharyya, Praveen K. Murthy, Edward...
The batch least-absolute shrinkage and selection operator (Lasso) has well-documented merits for estimating sparse signals of interest emerging in various applications, where obse...
Evolving Takagi Sugeno (eTS) models are optimised for use in applications with high sampling rates. This mode of use produces excellent prediction results very quickly and with lo...
Multi-dimensional systems containing nested loops are widely used to model scientific applications such as image processing, geophysical signal processing and fluid dynamics. Ho...
Ted Zhihong Yu, Edwin Hsing-Mean Sha, Nelson L. Pa...