We formulate and study distributed estimation algorithms based on diffusion protocols to
implement cooperation among individual adaptive nodes. The individual nodes are …

This book was written in response to the growing demand for a text that provides a unified
treatment of linear and nonlinear complex valued adaptive filters, and methods for the …

This paper introduces a new class of nonlinear adaptive filters, whose structure is based on
Hammerstein model. Such filters derive from the functional link adaptive filter (FLAF) model …

Adaptive networks rely on in-network and collaborative processing among distributed
agents to deliver enhanced performance in estimation and inference tasks. Information is …

Enormous amounts of dynamic observation and measurement data are collected from
sensors in Wireless Sensor Networks (WSNs) for the Internet of Things (IoT) applications …

In this work, we analyze the mean-square performance of different strategies for distributed
estimation over least-mean-squares (LMS) adaptive networks. The results highlight some …

Large volumes of real-world observation and measurement data are collected from sensory
devices in the Internet of Things (IoT) networks. IoT data is often generated in highly …

The combination of filters concept is a simple and flexible method to circumvent various
compromises hampering the operation of adaptive linear filters. Recently, applications which …

Blind equalizers avoid the transmission of pilot sequences, allowing a more efficient use of
the channel bandwidth. Normally, after a first rough equalization is achieved, it is necessary …

When the noise is second-order (SO) noncircular, the least stochastic entropy (LSE)
algorithm can obtain a low steady-state misalignment compared to the complex-valued least …