14 citations · 22 across the 7 of their papers we have counts for
6 papers · 1 filter
Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation
Guilherme Ramos, A. Pedro Aguiar, Soummya Kar +1
Average consensus protocols emerge with a central role in distributed systems and decision-making such as distributed information fusion, distributed optimization, distributed esti…
Fractional cyber-neural systems -- a brief survey
Emily Reed, Sarthak Chatterjee, Guilherme Ramos +2
Neurotechnology has made great strides in the last 20 years. However, we still have a long way to go to commercialize many of these technologies as we lack a unified framework to s…
Minimum jointly structural input and output selection for strongly connected networks
Guilherme Ramos, A. Pedro Aguiar, Sérgio Pequito
In this paper, given a linear time-invariant strongly connected network, we study the problem of determining the minimum number of state variables that need to be simultaneously ac…
Minimum Structural Sensor Placement for Switched Linear Time-Invariant Systems and Unknown Inputs
Emily A. Reed, Guilherme Ramos, Paul Bogdan +1
In this paper, we study the structural state and input observability of continuous-time switched linear time-invariant systems and unknown inputs. First, we provide necessary and s…
A Discrete-time Reputation-based Resilient Consensus Algorithm for Synchronous or Asynchronous Communications
Guilherme Ramos, Daniel Silvestre, Carlos Silvestre
We tackle the problem of a set of agents achieving resilient consensus in the presence of attacked agents. We present a discrete-time reputation-based consensus algorithm for synch…
A scalable distributed dynamical systems approach to compute the strongly connected components and diameter of networks
Emily A. Reed, Guilherme Ramos, Paul Bogdan +1
Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of discrete optimization problems, and subsequently, machine learni…