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Minimum Mean-Square Error Equalization for Second-Order Volterra Systems

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Multitask Diffusion Adaptation Over Networks

Ali H. Sayed, Jie Chen

Adaptive networks are suitable for decentralized inference tasks. Recent works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are man ...

Institute of Electrical and Electronics Engineers2014

Hydrodynamic model calibration from pattern recognition of non-orthorectified terrestrial photographs

Paolo Perona

This paper presents a remote sensing technique for calibrating hydrodynamics models, which is particularly useful when access to the riverbed for a direct measure of flow variables may be precluded. The proposed technique uses terrestrial photography and a ...

Elsevier2014

Estimation of Space-Time Varying Parameters Using a Diffusion LMS Algorithm

Ali H. Sayed

We study the problem of distributed adaptive estimation over networks where nodes cooperate to estimate physical parameters that can vary over both space and time domains. We use a set of basis functions to characterize the space-varying nature of the para ...

2014

Bayesian Denoising: From MAP to MMSE Using Consistent Cycle Spinning

Michaël Unser, Emrah Bostan, Ulugbek Kamilov, Abbas Kazerouni

We introduce a new approach for the implementation of minimum mean-square error (MMSE) denoising for signals with decoupled derivatives. Our method casts the problem as a penalized least-squares regression in the redundant wavelet domain. It exploits the l ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

Michaël Unser, Arash Amini, Emrah Bostan, Ulugbek Kamilov

We consider continuous-time sparse stochastic processes from which we have only a finite number of noisy/noiseless samples. Our goal is to estimate the noiseless samples (denoising) and the signal in-between (interpolation problem). By relying on tools fro ...

Ieee-Inst Electrical Electronics Engineers Inc2013

MMSE Estimation of Sparse Levy Processes

Michaël Unser, Arash Amini, Ulugbek Kamilov, Pedram Pad

We investigate a stochastic signal-processing framework for signals with sparse derivatives, where the samples of a Levy process are corrupted by noise. The proposed signal model covers the well-known Brownian motion and piecewise-constant Poisson process; ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Performance of diffusion adaptation for collaborative optimization

Ali H. Sayed

We derive an adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes. The cost function is assumed to consist of the sum of individual components, and diffusion adaptation is used to enable the nodes t ...

IEEE2012

Performance Limits for Distributed Estimation Over LMS Adaptive Networks

Ali H. Sayed

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 useful properties for distributed adaptation in comparison to fusion-based c ...

IEEE2012

Message-Passing De-Quantization with Applications to Compressed Sensing

Ulugbek Kamilov

Estimation of a vector from quantized linear measurements is a common problem for which simple linear techniques are suboptimal—sometimes greatly so. This paper develops message-passing de-quantization (MPDQ) algorithms for minimum mean-squared error estim ...

IEEE2012

Distributed moving horizon estimation for nonlinear constrained systems

Giancarlo Ferrari Trecate, Marcello Farina

In this paper we consider a nonlinear constrained system observed by a sensor network and propose a distributed state estimation scheme based on moving horizon estimation (MHE). In order to embrace the case where the whole system state cannot be reconstruc ...

2012