18 accepted papers
While systems analysis has been studied for decades in the context of control theory, it has only been recently used to improve the convergence of Denoising Diffusion Probabilistic Models. This work describes a novel improvement to Third-Order Langevin Dynamics (TOLD), a recent diffusion method that…
In this paper, we propose Online LASSO - a version of LASSO that is configured for streaming data. In standard LASSO, the penalty parameter is typically chosen by cross-validation, a procedure which requires the entire dataset upfront and repeated fitting. The main contribution of this work is in fi…
An important and often overlooked aspect of particle filtering methods is the estimation of unknown static parameters. A simple approach for addressing this problem is to augment the unknown static parameters as auxiliary states that are jointly estimated with the time-varying parameters of interest…
The curse of dimensionality has been a long-standing problem in the field of particle filters (PFs), and prevents their use in real complex systems characterized by large number of unknowns. Recently a two-stage PF (TPF) for high dimensions was proposed, showing promising results with modest number…
The quality of importance distribution is vital to adaptive importance sampling, especially in high dimensional sampling spaces where the target distributions are sparse and hard to approximate. This requires that the proposal distributions are expressive and easily adaptable. Because of the need fo…
Regime-switching state-space models (RS-SSMs) are an important class of statistical models that can be used to represent real-world phenomena. Unlike regular state-space models, RS-SSMs allow for dynamic uncertainty in the state transition and observations distributions, making them much more expres…
In this paper, we propose a novel Bayesian approach for estimating a gene network’s topology using particle Gibbs sampling. The conditional posterior distributions of the unknowns in a state-space model describing the time evolution of gene expressions are derived and employed for exact Bayesian pos…
The population Monte Carlo (PMC) algorithm is a popular adaptive importance sampling (AIS) method used for approximate computation of intractable integrals. Over the years, many advances have been made in the theory and implementation of PMC schemes. The mixture PMC (M-PMC) algorithm, for instance,…
Altitude estimation is important for successful control and navigation of unmanned aerial vehicles (UAVs). UAVs do not have indoor access to GPS signals and can only use on-board sensors for reliable estimation of altitude. Unfortunately, most existing navigation schemes are not robust to the presen…
Adaptive importance sampling (AIS) methods are a family of algorithms which can be used to approximate Bayesian posterior distributions. Many AIS algorithms exist in the literature, where the differences arise in the manner by which the proposal distribution is adapted at each iteration. The adaptiv…
We present a novel Rao-Blackwellized multiple particle filtering method for inference of correlated latent states observed via nonlinear functions. We adopt a state-space framework and model the dynamic correlated states using a mixing matrix, embedded in white Gaussian noise. The critical challenge…
In this paper we report our efforts to streamline the curriculum of a lecture-based course on signals and systems with exercises using the Matlab computing environment. We use a computer framework to generate individualized variations of problems, which are assigned to teams of students as well as t…
In this paper, we consider state-space models where the latent processes represent correlated mixtures of fractional Gaussian processes embedded in white Gaussian noises. The observed data are nonlinear functions of the latent states. The fractional Gaussian processes have interesting properties inc…
We report on a university-based pilot initiative to introduce students in grades 9–12 to electrical engineering practices. The after-school program consisted of two modules of four two-hour sessions and targeted students from two different local schools. They were exposed to hands-on electronic acti…
In many practical scenarios, including those dealing with large data sets, calculating global estimators of unknown variables of interest becomes unfeasible. A common solution is obtaining partial estimators and combining them to approximate the global one. In this paper, we focus on minimum mean sq…
Particle filtering has been widely accepted as an important methodology for processing data represented by state-space models characterized by nonlinearities and/or non-Gaussianities. It is also well documented that particle filtering deteriorates quickly in performance when the dimension of the tra…
This paper presents an analysis on optimal mobile sensor configuration for multiple-target-tracking (MTT) with Received-Signal-Strength-Indicator (RSSI) based measurements. The analysis is based on the underlying assumption that the complexity of this inherently high-dimensional problem is reduced b…
We investigate the problem of real-time self-tracking of tagged objects in a new system with low-cost “smart” tags. These tiny and battery-less devices will play a pivotal role in the infrastructure of the Internet of Things (IoT). With capabilities of low-power computation and tag-to-tag backscatte…