This paper discusses the problem of tracking a moving target by means of a cluster of mobile agents that is able to sense the acoustic emissions of the target, with the aim of improving the target localization and tracking performance with respect to conventional fixed-array acoustic localization. We handle the acoustic part of the problem by modeling the cluster as a sensor network, and we propose a centralized control strategy for the agents that exploits the spatial sensitivity pattern of the sensor network to estimate the best possible cluster configuration with respect to the expected target position. In order to take into account the position estimation delay due to the frame-based nature of the processing, the possible positions of the acoustic target in a given future time interval are represented in terms of a compatible set, that is, the set of all possible future positions of the target, given its dynamics and its present state. A frame-by-frame cluster reconfiguration algorithm is presented, which adapts the position of each sensing agent with the goal of pursuing the maximum overlap between the region of high acoustic sensitivity of the entire cluster and the compatible set of the sound-emitting target. The tracking scheme iterates, at each observation frame, the computation of the target compatible set, the reconfiguration of the cluster, and the target acoustic localization. The reconfiguration step makes use of an opportune cost function proportional to the difference of the compatibility set and the acoustic sensitivity spatial pattern determined by the mobile agent positions. Simulations under different geometric configurations and positioning constraints demonstrate the ability of the proposed approach to effectively localize and track a moving target based on its acoustic emission. The Doppler effect related to moving sources and sensors is taken into account, and its impact on performance is analyzed. We compare the localization results with conventional static-array localization and positioning of acoustic sensors through genetic algorithm optimization, and results demonstrate the sensible improvements in terms of localization and tracking performance. Although the method is discussed here with respect to acoustic target tracking, it can be effectively adapted to video-based localization and tracking, or to multimodal information settings (e.g., audio and video).

We performed a mathematical analysis of the dynamic control loops regulating the vasomotor tone of vascular smooth muscle, blood volume, and mean arterial pressure, which involve the arginine vasopressin (AVP) system, the atrial natriuretic peptide system (ANP), and the renin-angiotensin-aldosterone system (RAAS). Our loop analysis of the AVP-ANP-RAAS system revealed the concurrent presence of two different regulatory mechanisms, which perform the same qualitative function: one affects blood pressure by regulating vasoconstriction, the other by regulating blood volume. Both the systems are candidate oscillators consisting of the negative-feedback loop of a monotone system: they admit a single equilibrium that can either be stable or give rise to oscillatory instability. Also a subsystem, which includes ANP and AVP stimulation of vascular smooth muscle cells, turns out to be a candidate oscillator composed of a monotone system with multiple negative feedback loops, and we show that its oscillatory potential is higher when the delays along all feedback loops are comparable. Our results give insight into the physiological mechanisms ruling long-term homeostasis of blood hydraulic parameters, which operate based on dynamical loops of interactions.

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.

An enhancement to the CSMA p-persistent protocol family is proposed, based on a fully decentralized control that adjusts the message transmission rate of each node to the estimated density of surrounding transmitting nodes. The system does not require enumeration of nodes nor control messages, the only input to the control coming from the physical medium occupation. In addition to communication protocols, this technique can be used as inexpensive and efficient density estimation of physical entities in highly dynamic scenarios. Stability conditions are proposed by means of a rigorous theoretical investigation. We prove that our algorithm ensures stability even with unknown and time--varying network topologies. We show that good channel exploitation levels can be assured as well by suitably tuning the control parameters. A digital algorithm for practical implementation is proposed. The results are supported by simulation experiments.

In this paper the indoor wireless localization problem is addressed both from the theoretical and application standpoints. The main result of the paper is on the theoretical side: the topological definition of regular and irregular nodes is introduced, and formal results are presented to support regularity as a desirable network property for the attainment of precise node localization. In force of this definition, a mixed convex/non-convex optimization approach has been derived for the solution of the positioning problem. The two procedures, suitably combined, allow the achievement of better convergence towards the best positioning of a multitude of blind wireless nodes. A completely decentralized, partially asynchronous algorithm is presented, which proceeds locally on each node based on the sole knowledge of the distances measured from, and of the estimated positions of the connected nodes only. Its repeated asynchronous application on each nodes guarantees the convergence of the algorithm to the positioning of the whole network, even in presence of a limited number of peripheral reference points. Indeed, no global information is required for the proper functioning of the algorithm. Simulations of relevant case studies have been performed to qualify the proposed scheme in realistic conditions, and the results are presented.

In this paper, we consider different aspects of the problem of controlling a multi-inventory system in the presence of uncertain demand and setups. The demand is unknown but bounded in an assigned compact set. The control input is assumed to be constant in its operating regime and to incur setup whenever a variation of this regime is required. Both setup times and setup configurations are unknown. We provide necessary and sufficient stabilizability conditions which turn out to be the same in the case in which there are no setups. Stabilization can be achieved, provided that the planning horizon is large enough and a computable lower bound is given. We also face the problem of ultimately confining the state in an assigned constraint set and provide conditions on this set for the problem to be feasible. Furthermore, we consider the case in which the controls are quantized, as in the case of systems which work in a switching mode. Finally, we deal with the case in which multiple setups may happen during the planning horizon.

Research on congestion-control algorithms has traditionally focused more on performance than on robustness of the closed-loop system to changes in network conditions. As the performance of the control loop is strictly connected with the quality of service, these systems are natural candidates to be approached by the optimal control theory. Unfortunately, this approach may fail in the presence of transmission delay variations, which are unavoidable in telecommunication systems. In this paper, we first show the fragility of optimal controllers and demonstrate their instability when the control delay is not known exactly. Then we propose a robust control algorithm based on a classical proportional integral derivative scheme which does not suffer from this fragility phenomenon. Its stability versus the control delay variations, as well as versus sources that transmit less than their computed share, is studied with Nyquist analysis. The control algorithm is implemented within a simulator in the framework of the asynchronous transfer mode (ATM) ABR transfer capability. The final part of the paper shows some selected results assessing the performance of the control algorithm in a realistic network environment. ABR was chosen as an example, but the control studied here can be applied in any data network to obtain a robust and reliable congestion-control scheme.

A class of production-distribution problems with unknown-but-bounded uncertain demand is considered. At each time, the demand is unknown, but each of its components is assumed to belong to an assigned interval. Furthermore, the system has production, transportation, and storage capacity constraints. The paper extends previous results to the case in which transportation delays are present. We show that the problem of finding a strategy which keeps bounded the storage levels reduces to that of finding a strategy for the associated instantaneous network, which is the network obtained by setting all the delays to zero in the original system. The state variables of the associated instantaneous network are the "inventory positions," given by the goods actually present in the warehouses plus the goods already ordered and leading to them.

We consider multiinventory production systems with control and state constraints dealing with unknown demand or supply levels. Unlike most contributions in the literature concerning this class of systems, we cope with uncertainties in an "unknown-but-bounded" fashion, in the sense that each unknown quantity may take any value in an assigned interval. For these situations, we perform a worst-case analysis. We show that a "smallest worst-case inventory level" exists, and it is associated to a steady-state control strategy. Then we consider the problem of driving the inventory levels to their smallest worst-case values. For this problem, we first give necessary and sufficient conditions, then we show that convergence occurs in a finite number of steps, and we give an upper bound for such a number.

We consider a dynamic network flow model for the control problem of a production-distribution system with periodic demand in the presence of storage and transportation capacity constraints. Unlike most papers dealing with control problems of dynamic networks, we derive a control strategy in feedback form. It is optimal in the sense that it involves, for any initial time, the set of all the initial states for which there exists a control strategy allowing the network variables (flows, storage levels) to remain in their constraint domain for all future times. The evaluation of such a strategy requires the previous computation of these maximal sets. We show that, due to the particular structure of the system, they are submodular polyhedra; in particular, they are finitely represented and the representation complexity is a priori known. Moreover, the evaluation of this sequence, even for the infinite horizon problem, can be performed in a finite number of steps that has a known upper bound depending on the dimension of the problem only. As a consequence of these results, the optimal strategy requires, at each step, to solve a submodular flow problem. The finite horizon problem is solved by the proposed method as a particular case. © 1996 John Wiley & Sons, Inc.