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This paper presents a new analytical method to simply estimate the integrated thermal noise in switched-capacitor filters (SCF). It is shown how the Bode theorem, which is theoretically valid only for continuous-time passive filters, can be extended to als ...
In this paper, we propose a model order reduction framework for parametrized quadratic optimization problems constrained by nonlinear stationary PDEs. Once the solutions of the optimization problem are characterized as the solutions of the corresponding op ...
This paper proposes a tradeoff between sample complexity and computation time that applies to statistical estimators based on convex optimization. As the amount of data increases, we can smooth optimization problems more and more aggressively to achieve ac ...
In this paper, a finite element error analysis is performed on a class of linear and nonlinear elliptic problems with small uncertain input. Using a perturbation approach, the exact (random) solution is expanded up to a certain order with respect to a para ...
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 ...
In this work, a diffusion-type algorithm is proposed to solve multitask estimation problems where each cluster of nodes is interested in estimating its own optimum parameter vector in a distributed manner. The approach relies on minimizing a global mean-sq ...
We discuss the design of sampled-data economic nonlinear model predictive control schemes for continuous-time systems. We present novel sufficient convergence conditions that do not require any kind of terminal constraints nor terminal penalties. Instead, ...
Super-resolution is a set of techniques used to enhance resolution of image. This work focuses on super resolution used in fluorescent microscopy, ex to image structures in the living cell. In this report we present new method for this task, both in one di ...
We propose a Bayesian approach where the signal structure can be represented by a mixture model with a submodular prior. We consider an observation model that leads to Lipschitz functions. Due to its combinatorial nature, computing the maximum a posteriori ...
In distributed inference, local cooperation among network nodes can be exploited to enhance the performance of each individual agent, but a challenging requirement for networks operating in dynamic real-world environments is that of adaptation. The interpl ...
