Publication

Polynomial Filter Design for Quantized Consensus

Related publications (30)

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Association for the Advancement of Artificial Intelligence (AAAI)2023

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Michel Bierlaire

In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...

2023

Worst-Case Delay Analysis of Time-Sensitive Networks with Network Calculus

Seyed Mohammadhossein Tabatabaee

Time-sensitive networks, as in the context of IEEE Time-Sensitive Networking (TSN) and IETF Deterministic Networking (DetNet), offer deterministic services with guaranteed bounded latency in order to support safety-critical applications. In this thesis, we ...

EPFL2023

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Melanie Nicole Zeilinger, Danilo Saccani

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

IEEE2023

Equilibria in Network Constrained Energy Markets

Leonardo Massai

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits. The energy market is modeled by a graph representing a constrained power network where nodes represent the markets and lin ...

Elsevier2023

Euclid preparation XXVI. The Euclid Morphology Challenge: Towards structural parameters for billions of galaxies

Slobodan Ilic, Georges Meylan, Frédéric Courbin, Richard Massey, Maurizio Martinelli, Gianluca Castignani, Marcello Farina, Yi Wang

The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morpholo ...

2023

Distributionally Robust Linear Quadratic Control

Daniel Kuhn, Cagil Kocyigit, Bahar Taskesen

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

2023

A multichannel versatile brain activity classification and closed loop neuromodulation system, device and method using a highly multiplexed mixed-signal front-end

Mahsa Shoaran, Uisub Shin, Bingzhao Zhu

A closed-loop neuromodulation system, including an electrode array that is implantable to a brain of a subject, analog front-end device (AFD) for selectively selecting and reading a plurality of channels from electrode array, a finite impulse response (FIR ...

2023

Toward Band n78 Shear Bulk Acoustic Resonators Using Crystalline Y-Cut Lithium Niobate Films With Spurious Suppression

Victor Plessky, Luis Guillermo Villanueva Torrijo, Soumya Yandrapalli

This work presents the study of suspended Y-cut Lithium Niobate shear bulk acoustic resonators for wide band filter applications in the frequency range of 3.5-4.5GHz. The resonators consist of a Lithium Niobate film with Aluminum top interdigitated electro ...

Institute of Electrical and Electronics Engineers2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023