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Synchronous distributed algorithms are easier to design and prove correct than algorithms that tolerate asynchrony. Yet, in the real world, networks experience asynchrony and other timing anomalies. In this paper, we address the question of how to efficien ...
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We show how to express an arbitrary integer interval I=[0,H] as a sumset I=∑i=1ℓGi∗[0,u−1]+[0,H′] of smaller integer intervals for some small values ℓ, u, and H′<u−1, where b∗A={ba:a∈A} and $A + B = ...
Stochastic modeling is a challenging task for low-cost sensors whose errors can have complex spectral structures. This makes the tuning process of the INS/GNSS Kalman filter often sensitive and difficult. For example, first-order Gauss–Markov processes are ...
Many algorithms have recently been proposed for finding communities in networks. By definition, a community is a subset of vertices with a high number of connections among the vertices, but only few connections with other vertices. The worst drawback of mo ...
This paper presents a framework for analyzing probabilistic safety and reachability problems for discrete time stochastic hybrid systems in scenarios where system dynamics are affected by rational competing agents. In particular, we consider a zero-sum gam ...
The efficient synthesis of circuits is a well-studied problem. Due to the NP-hardness of the problem, no optimal algorithm has been presented so far. However, the heuristics presented by several researchers in the past, which are also adopted by commercial ...
We consider the problem of classification of multiple observations of the same object, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all unlabelled examples belong to the same unknown cla ...
Concurrently exploring both algorithmic and architectural optimizations is a new design paradigm. This survey paper addresses the latest research and future perspectives on the simultaneous development of video coding, processing, and computing algorithms ...
We study the problem of distributed state-space estimation, where a set of nodes are required to estimate the state of a nonlinear state-space system based on their observations. We extend our previous work on distributed Kalman filtering to the nonlinear ...
Compressive sensing (CS) is a data acquisition and recovery technique for finding sparse solutions to linear inverse problems from sub-Nyquist measurements. CS features a wide range of computationally efficient and robust signal recovery methods, based on ...
