Algorithm and VLSI architecture for linear MMSE detection in MIMO-OFDM systems

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A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

The p-Laplacian problem -del & sdot; ((mu + |del u|(p-2))del u) = f is considered, where mu is a given positive number. An anisotropic a posteriori residual-based error estimator is presented. The error estimator is shown to be equivalent, up to higher ord ...

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

The objective of this study is to address the difficulty of simplifying the geometric model in which a differential problem is formulated, also called defeaturing, while simultaneously ensuring that the accuracy of the solution is maintained under control. ...

Local modifications of a computational domain are often performed in order to simplify the meshing process and to reduce computational costs and memory requirements. However, removing geometrical features of a domain often introduces a non-negligible error ...

We introduce a distributionally robust minimium mean square error estimation model with a Wasserstein ambiguity set to recover an unknown signal from a noisy observation. The proposed model can be viewed as a zero-sum game between a statistician choosing a ...

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

The increasing availability of sensing techniques provides a great opportunity for engineers to design state estimation methods, which are optimal for the system under observation and the observed noise patterns. However, these patterns often do not fulfil ...

We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

Trimming consists of cutting away parts of a geometric domain, without reconstructing a global parametrization (meshing). It is a widely used operation in computer-aided design, which generates meshes that are unfitted with the described physical object. T ...

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The quantification of uncertainties can be particularly challenging for problems requiring long-time integration as the structure of the random solution might considerably change over time. In this respect, dynamical low-rank approximation (DLRA) is very a ...

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Inspired by Sibson’s alpha-mutual information, we introduce a new parametric class of universal predictors. This class interpolates two well-known predictors, the mixture estimator, that includes the Laplace and the Krichevsky-Trofimov predictors, and the ...

Removing geometrical details from a complex domain is a classical operation in computer aided design for simulation and manufacturing. This procedure simplifies the meshing process, and it enables faster simulations with less memory requirements. But depen ...

LoRaWAN is a low-power wireless technology that provides long-range connectivity to battery-powered Internet of Things (IoT) devices. To minimize the energy consumption of the IoT nodes, LoRaWAN networks use for the uplink a pure non-slotted ALOHA multiple ...

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

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Algorithm and VLSI architecture for linear MMSE detection in MIMO-OFDM systems

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Spectral Estimators for High-Dimensional Matrix Inference

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

An equilibrated flux a posteriori error estimator for defeaturing problems

A Shape Derivative Approach to Domain Simplification

Analysis-aware defeaturing of complex geometries with Neumann features

Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Regularization for distributionally robust state estimation and prediction

ReLU Neural Network Galerkin BEM

An a posteriori error estimator for isogeometric analysis on trimmed geometries

Parametric Estimation For Functional Autoregressive Processes On The Sphere

Dynamical low rank approximation for uncertainty quantification of time-dependent problems

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Alpha-NML Universal Predictors

Analysis-aware defeaturing of complex geometries

Adaptive analysis-aware defeaturing

Enhancing the Reliability of Dense LoRaWAN Networks With Multi-User Receivers

Sparsely Observed Functional Time Series: Theory and Applications

A probabilistic finite element method based on random meshes: A posteriori error estimators and Bayesian inverse problems