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In this article, we address the problem of singular value decomposition of polynomial matrices and eigenvalue decomposition of para-Hermitian matrices. Discrete Fourier transform enables us to propose a new algorithm based on uniform sampling of polynomial matrices in frequency domain. This formulation of polynomial matrix decomposition allows for controlling spectral properties of the decomposition. We set up a nonlinear quadratic minimization for phase alignment of decomposition at each frequency sample, which leads to a compact order approximation of decomposed matrices. Compact order approximation of decomposed matrices makes it suitable in filterbank and multiple-input multiple-output (MIMO) precoding applications or any application dealing with realization of polynomial matrices as transfer function of MIMO systems. Numerical examples demonstrate the versatility of the proposed algorithm provided by relaxation of paraunitary constraint, and its configurability to select different properties.

Abstract. Building distributed content-based publish/subscribe systems has remained a challenge. Existing solutions typically use a relatively small set of trusted computers as brokers, which may lead to scalability concerns for large Internet-scale workloads. Moreover, since each broker maintains state for a large number of users, it may be difficult to tolerate faults at each broker. In this paper we propose an approach to building content-based publish/subscribe systems on top of distributed hash table (DHT) systems. DHT systems have been effectively used for scalable and fault-tolerant resource lookup in large peer-to-peer networks. Our approach provides predicate-based query semantics and supports constrained range queries. Experimental evaluation shows that our approach is scalable to thousands of brokers, although proper tuning is required.

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