Particle filters are sequential Monte Carlo methods that are used in numerous problems
where time-varying signals must be presented in real time and where the objective is to …

We propose new Bayesian algorithms to automatically track current dipole sources of neural
activity in real time. We integrate multiple particle filters to track the dynamic parameters of a …

In this paper, we propose a compact threshold-based resampling algorithm and architecture
for efficient hardware implementation of particle filters (PFs). By using a simple threshold …

Abstract Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-
Gaussian dynamic system parameters. As these algorithms are recursive, their real-time …

Target tracking, as defined in this thesis, is estimating moving targets' states using noisy
measurements obtained at a single observation point or node. The target states consist of …

Accepted: 12 July 2019 The localization of neural sources is a crucial issue in medical,
scientific and technical applications. However, the dipole sources in the brain may vary in …

This paper deals with the real-time FPGA implementation of the posterior system state
estimation in dynamic state-space models using a particle filter. The system is constructed …

In this paper, we propose a compact threshold-based resampling algorithm and architecture
for efficient hardware implementation of particle filters. By using a simple threshold-based …

FIR filters occupy a central place many signal processing applications which either alter the
shape, frequency or the sampling frequency of the signal. FIR filters are used because of …

Real-time image processing with a compact FPGA-based architecture plays a key role in
dynamic state-space models. This paper presents an energy efficient FPGA acceleration …