Principal component analysis (PCA) is a fundamental primitive of many data analysis, array
processing, and machine learning methods. In applications where extremely large arrays of …

Inspired by ongoing evolutions in the field of wireless body area networks (WBANs), this
tutorial paper presents a conceptual and exploratory study of wireless …

The problem of choosing the best subset of sensors that guarantees a certain estimation
performance is referred to as sensor selection. In this paper, we focus on observations that …

Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts
latent principal factors that contribute to the most variation of the data. When data are stored …

Large-scale processes have become common, and fault detection for such processes is
imperative. This work studies the data-driven distributed local fault detection problem for …

This paper studies the problem of data-adaptive representations for big, distributed data. It is
assumed that a number of geographically-distributed, interconnected sites have massive …

The adoption of blockchain for Transactive Energy has gained significant momentum as it
allows mutually non-trusting agents to trade energy services in a trustless energy market …

We recently proposed an algorithmic framework, distributed Banach-Picard iteration (DBPI),
allowing a set of agents linked by a communication network to find a fixed point of a map …

Wireless sensor networks (WSNs) are becoming more and more a pervasive tool to monitor
a wide range of physical phenomena. The opportunities arising from the many potential …

Platforms which combine data mining algorithms and interactive visualizations play a key
role in the discovery process from complex networks data, eg Web and Online Social …