In a network, a distributed consensus algorithm is fully characterized by its weighting matrix. Although there exist numerical methods for obtaining the optimal weighting matrix, we have not found an in-network implementation of any of these methods that works for all network topologies. In this paper, we propose an in-network algorithm for finding such an optimal weighting matrix.

We present an application of distributed consensus algorithms to metamorphic systems. A metamorphic system is a set of identical units that can self-assemble to form a rigid structure. For instance, one can think of a robotic arm composed of multiple links connected by joints. The system can change its shape in order to adapt to different environments via reconfiguration of its constituting uni...

We compare, in terms of solvability and efficiency, the synchronous model, noted SS, with the asynchronous model augmented with a perfect failure detector, noted SP . We first exhibit a problem that, although timefree, is solvable in SS but not in SP . We then examine whether one of these two models allows more efficient solutions for designing fault-tolerant applications. In particular, we con...

Meaningfully integrating massive multi-experimental genomic data sets is becoming critical for the understanding of gene function. We have recently proposed methodologies for integrating large numbers of microarray data sets based on consensus clustering. Our methods combine gene clusters into a unified representation, or a consensus, that is insensitive to mis-classifications in the individual...

In this paper, a new distributed receding horizon formation control scheme is introduced using relative dynamical model instead of absolute dynamical model of each member robot in most existing formation control algorithms. Convergence of the proposed distributed receding horizon formation control (DRHFC) is implemented using some additional control input constraints, and the relative dynamical...

In this paper, we propose a distributed max–min consensus algorithm for a discrete-time n-node system. Each node iteratively updates its state to a weighted average of its own state together with the minimum and maximum states of its neighbors. In order for carrying out this update, each node needs to know the positive direction of the state axis, as some additional information besides the rela...

This paper compares a new algorithm with two well-known algorithms for precise alignment of overlapping adjacent images. The new part of the algorithm is concerned with the selection of exactly matching pairs of feature points in the two images and its performance is compared with the performance of the Least Median of Square regression algorithm (LMedS), and the Random Sample Consensus (RANSAC...

We study average consensus when, simultaneously, the topology is random (links are offline or onlineat random times) and the communication among sensors is corrupted by additive noise. Additive noisecauses the states of the standard average consensus algorithm to diverge. To overcome this, we considertwo modifications to average consensus: 1) the A−ND algorithm with weights decaying...