Predefined-Time Formation Control for Multiagent Systems-Based on Distributed Optimization.

This article presents a solution to the leaderless formation control problem for first-order multiagent systems, which minimizes a global function composed of a sum of local strongly convex functions for each agent under weighted undirected graphs within a predefined time. The proposed distributed optimization process consists of two steps: 1) the controller initially leads each agent to the minimizer of its local function and 2) then guides all agents toward achieving leaderless formation and reaching the global function's minimizer. The proposed scheme requires fewer adjustable parameters than most existing methods in the literature without the need for auxiliary variables or time-variable gains.

A dynamical approach to generate chaos in a micromechanical resonator.

Chaotic systems, presenting complex and nonreproducible dynamics, may be found in nature, from the interaction between planets to the evolution of weather, but can also be tailored using current technologies for advanced signal processing. However, the realization of chaotic signal generators remains challenging due to the involved dynamics of the underlying physics. In this paper, we experimentally and numerically present a disruptive approach to generate a chaotic signal from a micromechanical resonator.

Requirements and attributes of nano-resonator mass spectrometry for the analysis of intact viral particles.

When studying viruses, the most prevalent aspects that come to mind are their structural and functional features, but this leaves in the shadows a quite universal characteristic: their mass. Even if approximations can be derived from size and density measurements, the multi MDa to GDa mass range, featuring a majority of viruses, has so far remained largely unexplored. Recently, nano-electromechanical resonator-based mass spectrometry (NEMS-MS) has demonstrated the ability to measure the mass of intact DNA filled viral capsids in excess of 100 MDa.

Optomechanical mass spectrometry.

Nanomechanical mass spectrometry has proven to be well suited for the analysis of high mass species such as viruses. Still, the use of one-dimensional devices such as vibrating beams forces a trade-off between analysis time and mass resolution. Complex readout schemes are also required to simultaneously monitor multiple resonance modes, which degrades resolution.

Self adaptive learning scheme for early diagnosis of simple and multiple switch faults in multicellular power converters.

This paper proposes a scheme based on the use of unsupervised machine learning approach and a drift detection mechanism in order to perform an early fault diagnosis of simple and multiple stuck-opened/stuck-closed switches in multicellular converters. Only the data samples representing the normal operation conditions are used in order to be adapted to the case where no data is available about faulty behaviors. A health indicator measuring the dissimilarity between normal and current operation conditions is built in order to detect a drift (degradations) in early stage.