In this paper we apply a novel approach to near-infrared subcutaneous palm vascular pattern authentication. The proposed method relies on a recursive algorithm based on a positive linear dynamical system whose evolution depends on the two matrices representing the vein patterns to be compared. The output of the system reaches a high value when a good matching between the two matrices is observed, otherwise it converges rapidly to zero, even in presence of noise. With respect to another algorithm we recently introduced, this approach achieves not only a better authentication performance but also a drastic reduction in terms of computation time. These improvements are demonstrated by means of extensive experiments conducted on challenging datasets.

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We consider the problem of controlling the transmission rate in a communication network where each node adjusts its own transmission rate exclusively based on the physical medium occupation (the band occupancy of the aggregate complementary nodes). We show how to design a decentralized control for maximizing band occupancy and fairness. If the network is fully connected, the problem admits a simple solution. Difficulties arise in the case of partially connected networks, and in the presence of time-varying network topologies and delays. General conditions are given which, by properly tuning control parameters, ensure stability. These conditions are conservative and affect system performance. However, we show that in the case of symmetric connections, stability can be studied based on the system eigenvalues even in the presence of topology switchings. Less conservative bounds can be inferred by exploiting known properties of the eigenvalues of the adjacency matrix of a graph. We finally consider the multichannel case, in which nodes may jump among channels: The previous scheme can be extended to this case and asymptotically ensures uniform channel exploitation.

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Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself.

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We consider the flow control problem for a general class of compartmental nonlinear systems, which can be associated with a graph whose nodes represent subsystems with their own internal dynamics, and whose arcs represent flow links among them. We consider a network-decentralized control: each agent controls a link between two nodes and decides its actions based on the states of these nodes only. We first provide general necessary and sufficient stabilizability conditions, proving that suitable network-decentralized strategies assure robust stability. We also show that, if all the subsystems at the nodes are marginally stable, a proper network-decentralized strategy asymptotically assures the minimum-norm flow, without requiring communication among agents.@en

The problem of synthesizing network-decentralized observers arises when several agents, corresponding to the nodes of a network, exchange information about local measurements to asymptotically estimate their own state. The network topology is unknown to the nodes, which can rely on information about their neighboring nodes only. For homogeneous systems, composed of identical agents, we show that a network-decentralized observer can be designed by starting from local observers (typically, optimal filters) and then adapting the gain to ensure overall stability. The smallest eigenvalue of the so-called generalized Laplacian matrix is crucial: stability is guaranteed if the gain is greater than the inverse of this eigenvalue, which is strictly positive if the graph is externally connected. To deal with uncertain topologies, we characterize the worst-case smallest eigenvalue of the generalized Laplacian matrix for externally connected graphs, and we prove that the worst-case graph is a chain. This general result provides a bound for the observer gain that ensures robustness of the network-decentralized observer even under arbitrary, possibly switching, configurations, and in the presence of noise.

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Most of the existing techniques for palmprint recognition are based on metrics that evaluate the distance between a pair of features. These metrics are typically based on static functions. In this paper we propose a new technique for palmprint recognition based on a dynamical system approach, focusing on preliminary experimental results. The essential idea is that the procedure iteratively eliminates points in both images to be compared which do not have enough close neighboring points in the image itself and in the comparison image. As a result of the iteration, in each image the surviving points are those having enough neighboring points in the comparison image. Our preliminary experimental results show that the proposed dynamic algorithm is competitive and slightly outperforms some state-of-the-art methods by achieving a higher genuine acceptance rate.

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We consider a traffic control problem defined on a network graph, whose nodes represent buffers and whose arcs represent flow channels. We consider network models with a peculiar aspect: each element of the flow arriving at each node must be redirected towards a precise other node of the network, hence each buffer is naturally split in several queues, characterized according to statistics about the flow splitting at the nodes. Precisely, each node is modelled as a Markov chain, in which some states are specifically associated with the arcs leaving the node: state j represents the amount of traffic waiting to be directed through arc j. We show that such a network can be stabilized by means of a network-decentralized control, in which the flow through each arc is controlled by an agent which only knows the congestion situation at the nodes it connects. The main result is that the proposed network-decentralized strategy is robust (namely it assures stability under all possible values of the Markov chain parameters) provided that zero is a simple eigenvalue for all the Markov chains, which includes the irreducible case.@en