A recursive deconvolution approach to disturbance reduction

Active noise control (ANC) uses an estimate of the noise affecting a system in order to remove its effect from the output. In some applications, it is possible to directly measure the noise and a feedforward compensator can be used. Other applications require instead that the noise be estimated from its effect on the system. This results in an adaptive feedback ANC, see a previous paper by Gan and Kuo. The identification of the disturbance from the output of the linear system is a deconvolution problem. In this paper, we study a deconvolution technique for the active reduction of the influence of the disturbance on the output of a linear control system. The regulator that we present does not affect the behavior of the system in the absence of disturbances.     

Performance of Parallel Concatenated Coding Schemes

In this paper, ensembles of parallel concatenated codes are studied and rigorous results on their asymptotic performance, under the assumption of maximum-likelihood (ML) decoding, are presented. In particular, it is proven that in any parallel concatenation scheme with k branches where all k encoders are recursive and the Bhattacharyya parameter of the channel is sufficiently small, the bit-error rate (BER) and the word-error rate go to 0 exactly like N ^1- k and N ^2- k, respectively. Different types of ensembles by changing the subgroup of permutations used to interconnect the various encoders, are considered.     

System-theoretic properties of convolutional codes over rings

Convolutional codes over rings are particularly suitable for representing codes over phase-modulation signals. In order to develop a complete structural analysis of this class of codes, it is necessary to study rational matrices over rings, which constitutes the generator matrices (encoders) for such convolutional codes. Noncatastrophic, minimal, systematic, and basic generator matrices are introduced and characterized by using a canonical form for polynomial matrices over rings. Finally, some classes of convolutional codes, defined according to the generator matrix they admit, are introduced and analyzed from a system-theoretic point of view     

Canonical kernel representations for behaviors over finite Abelian groups

In this paper the problem of constructing canonical kernel representations for systems whose alphabet set is an Abelian group is addressed. In standard linear system theory, there exist two important canonical kernel representations: minimal and row reduced kernel representations. In this paper we propose an extension of such canonical kernel representations to the group case. This topic has applications in the theory of convolutional codes over groups where kernel representations correspond to syndrome formers which are essential in decoding.     

Interconnections and symmetries of linear differential systems

In this paper we study the interplay between control problems and symmetries in the context of linear systems. In particular, we establish sufficient conditions under which it is possible to control a symmetric system in order to make it achieve control objectives, without breaking its symmetry.     

Listeria monocytogenes: Occurrence in beef and identification of the main contamination points in processing plants

This study aimed to establish the occurrence of Listeria spp., especially L. monocytogenes and its main serotypes, in beef and processing plants. A total of 443 samples were obtained from equipment, installations and products from 11 meat processing establishments from Paraná state, Brazil. All samples were analyzed using USDA methodology for Listeria spp. detection, followed by species identification. The occurrence of Listeria spp. in the samples was 38.1% of which 51.4% were from equipment, 35.4% from installations and 30.2% from products. The identified species were: L. monocytogenes (12.6%), L. innocua (78.4%), L. seeligeri (1.2%), L. welshimeri (7.2%) and L. grayi (0.6%). The identified serotypes of L. monocytogenes were 1/2a and 4b. The results demonstrate the significance of equipment and installations as sources of contamination by Listeria spp. and L. monocytogenes in the processing of beef and meat products.     

An operator-theoretic approach to the mixed-sensitivity minimization problem

We consider the mixed-sensitivity minimization problem (scalar case). It gives rise to the so-called two-block problem on the algebra H^∞; we analyze this problem from an operator point of view, using Krein space theory. We obtain a necessary and sufficient condition for the uniqueness of the solution and a parameterization of all solutions in the non-uniqueness case. Moreover, an interpolation interpretation is given for the finite-dimensional case.     

Randomized consensus algorithms over large scale networks

Various randomized consensus algorithms have been proposed in the literature. In some case randomness is due to the choice of a randomized network communication protocol. In other cases, randomness is simply caused by the potential unpredictability of the environment in which the distributed consensus algorithm is implemented. Conditions ensuring the convergence of these algorithms have already been proposed in the literature. As far as the rate of convergence of such algorithms, two approaches can be proposed. One is based on a mean square analysis, while a second is based on the concept of Lyapunov exponent. In this paper, by some concentration results, we prove that the mean square convergence analysis is the right approach when the number of agents is large. Differently from the existing literature, in this paper we do not stick to average preserving algorithms. Instead, we allow to reach consensus at a point which may differ from the average of the initial states. The advantage of such algorithms is that they do not require bidirectional communication among agents and thus they apply to more general contexts. Moreover, in many important contexts it is possible to prove that the displacement from the initial average tends to zero, when the number of agents goes to infinity.