Publications related to On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms

Expected loss analysis of thresholded authentication protocols in noisy conditions

Serge Vaudenay, Christos Dimitrakakis, Aikaterini Mitrokotsa

A number of authentication protocols have been proposed recently, where at least some part of the authentication is performed during a phase, lasting

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rounds, with no error correction. This requires assigning an acceptable threshold for the number of de ...

2010

Linear Compressive Networks

Michael Christoph Gastpar, Naveen Goela

A linear compressive network (LCN) is defined as a graph of sensors in which each encoding sensor compresses incoming jointly Gaussian random signals and transmits (potentially) low-dimensional linear projections to neighbors over a noisy uncoded channel. ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Distributed Karhunen-Loeve Transform With Nested Subspaces

Michael Christoph Gastpar, Naveen Goela

A network in which sensors observe a common Gaussian source is analyzed. Using a fixed linear transform, each sensor compresses its high-dimensional observation into a low-dimensional representation. The latter is provided to a central decoder that reconst ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime

Martin Vetterli, Olivier Roy

Distributed estimation of an unknown signal is a common task in sensor networks. The scenario usually envisioned consists of several nodes, each making an observation correlated with the signal of interest. The acquired data is then wirelessly transmitted ...

Institute of Electrical and Electronics Engineers2008

A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data

Henry Markram, Idan Segev

We present a novel framework for automatically constraining parameters of compartmental models of neurons, given a large set of experimentally-measured responses of these neurons. In experiments, intrinsic noise gives rise to a large variability (e.g., in ...

2007

Optimal control of mass transfer in peritoneal dialysis

Diego Mastalli

The aim of this work is to set up mathematical models and numerical methods to investigate the mass transfer process occurring during the peritoneal dialysis (PD) therapy. More precisely the final goal is the set up of tools to find for each patient submit ...

EPFL2006

Data-Driven Controller Tuning Using Frequency Domain Specifications

Alireza Karimi, Roland Longchamp, Daniel Garcia

A model-free PID controller tuning approach is presented in this paper. The method can handle different stability and performance indicators in the frequency domain. The phase margin, gain margin, crossover frequency and more advanced indicators which are ...

2006

Generalized Smoothing Splines and the Optimal Discretization of the Wiener Filter

Michaël Unser, Thierry Blu

We introduce an extended class of cardinal LL-splines, where L is a pseudo-differential operator satisfying some admissibility conditions. We show that the LL-spline signal interpolation problem is well posed and that its solution is the unique minimizer ...

IEEE2005

Least Mean-Phase Adaptive Filters With Application to Communications Systems

Ali H. Sayed

The mean-squared-error criterion is widely used in the literature. However, there are applications where the squared-error is not the primary parameter affecting the performance of a system. In many communication systems, for instance, the information bits ...

IEEE2004

Multigrid Image Reconstruction from Arbitrarily Spaced Samples

Michaël Unser, Michael Sühling, Muthuvel Arigovindan

We propose a novel multiresolution-multigrid based signal reconstruction method from arbitrarily spaced samples. The signal is reconstructed on a uniform grid using B-splines basis functions. The computation of spline weights is formulated as a variational ...

IEEE2002

On the selection of optimal nonlinearities for stochastic gradient adaptive algorithms

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Expected loss analysis of thresholded authentication protocols in noisy conditions

Linear Compressive Networks

Distributed Karhunen-Loeve Transform With Nested Subspaces

Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime

A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data

Optimal control of mass transfer in peritoneal dialysis

Data-Driven Controller Tuning Using Frequency Domain Specifications

Generalized Smoothing Splines and the Optimal Discretization of the Wiener Filter

Least Mean-Phase Adaptive Filters With Application to Communications Systems

Multigrid Image Reconstruction from Arbitrarily Spaced Samples

Linear Compressive Networks

Distributed Karhunen-Loeve Transform With Nested Subspaces

Optimal control of mass transfer in peritoneal dialysis

A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data

Generalized Smoothing Splines and the Optimal Discretization of the Wiener Filter

Expected loss analysis of thresholded authentication protocols in noisy conditions

Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime

Data-Driven Controller Tuning Using Frequency Domain Specifications

Least Mean-Phase Adaptive Filters With Application to Communications Systems

Multigrid Image Reconstruction from Arbitrarily Spaced Samples