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Found 10 papers, 3 papers with code
Online input design for discrimination of linear models using concave minimization
Stochastic Closed-Loop Active Fault Diagnosis (CLAFD) aims to select the input sequentially in order to improve the discrimination of different models by minimizing the predicted error probability.
In-sector Compressive Beam Alignment for MmWave and THz Radios
The essence of our framework lies in the construction of a low-resolution beam codebook to identify the best sector and in the design of the CS matrix for in-sector channel estimation.
Analysis of Orthogonal Matching Pursuit for Compressed Sensing in Practical Settings
Orthogonal matching pursuit (OMP) is a widely used greedy algorithm for sparse signal recovery in compressed sensing (CS).
Data-enabled predictive control with instrumental variables: the direct equivalence with subspace predictive control
Direct data-driven control has attracted substantial interest since it enables optimization-based control without the need for a parametric model.
Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation
Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams.
Nonuniform Defocus Removal for Image Classification
We propose and study the single-frame anisoplanatic deconvolution problem associated with image classification using machine learning algorithms, named the nonuniform defocus removal (NDR) problem.
QUARKS: Identification of large-scale Kronecker Vector-AutoRegressive models
In this paper we propose a Kronecker-based modeling for identifying the spatial-temporal dynamics of large sensor arrays.
Systems and Control
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
This recommendation is usually selected by optimizing a given acquisition function.
Online Optimization with Costly and Noisy Measurements using Random Fourier Expansions
This paper analyzes DONE, an online optimization algorithm that iteratively minimizes an unknown function based on costly and noisy measurements.
System Identification through Online Sparse Gaussian Process Regression with Input Noise
There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification.
