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We present an extension of Scala that supports constraint programming over bounded and unbounded domains. The resulting language, Kaplan, provides the benefits of constraint programming while preserving the existing features of Scala. Kaplan integrates con ...
List-decoding for arbitrarily varying channels (AVCs) under state constraints is investigated. It is shown that rates within of the randomized coding capacity of AVCs with input-dependent state can be achieved under maximal error with list-decoding using l ...
We present an extension of Scala that supports constraint programming over bounded and unbounded domains. The resulting language, Kaplan, provides the benefits of constraint programming while preserving the existing features of Scala. Kaplan integrates con ...
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This paper is devoted to the design of a predictive controller for constrained linear systems to track periodic references. The only assumption on the dynamics of the reference is that it is periodic and its period is known. It is also assumed that the ref ...
This work develops a decentralized adaptive strategy for throughput maximization over peer-to-peer (P2P) networks. The adaptive strategy can cope with changing network topologies, is robust to network disruptions, and does not rely on central processors. T ...
Real world planning applications typically involve making decisions that consumes limited resources, which requires both planning and scheduling. In this paper we propose a new approach that bridges the gap between planning and scheduling by explicitly mod ...
A robust PID controller design toolbox for Matlab is presented in this paper. The design is based on linearizing or convexifying the conventional non-convex constraints on the classical robustness margins or H∞ constraints. Then the existing optimization s ...
Recently, the optimisation of end-of-life (EOL) product recovery processes has been highlighted. At the inspection phase after disassembly, each part can have various recovery options such as reuse, reconditioning, remanufacturing, and disposal. Depending ...
Latent variable models provide valuable compact representations for learning and inference in many computer vision tasks. However, most existing models cannot directly encode prior knowledge about the specific problem at hand. In this paper, we introduce a ...
This paper considers linear discrete-time systems with additive bounded disturbances subject to hard control input bounds and constraints on the expected number of state-constraint violations averaged over time, or, equivalently, constraints on the probabi ...