2617queue lengths q1(t2N ), q2(t2N ) are equal to the optimal cyclic solutions and the condition (36) has to be satisfied. VI. CONCLUSIONFor the simplified isolated controlled intersection, in the case when the criterion J is a strictly increasing linear function of the queue lengths, we can compute the optimal switching sequence for the steady-state problem with constant cycle length by solving a LP problem analytically. A necessary and sufficient condition for the steady-state control with constant cycle length was also derived. The N-stages control problem was formulated. It is shown that the N-stages control problem can be solved by LP if the criterion J is linear and strictly increasing. Furthermore, N-stages control can be used to bring the queue lengths to optimum. REFERENCES[1] T.-H. Chang and J.-T. Lin, "Optimal signal timing for an oversaturated intersection," Transport. Res. B, vol. 34, no. 6, pp. 471-491, 2000. [2] B. De Schutter, "Optimizing acyclic traffic signal switching sequences through an extended linear complementarity problem formulation," Eur.Abstract-The Cucker-Smale (CS) flocking model is an interacting particle system, in which each particle updates its velocity by adding to it a weighted average of the differences of its velocity with those of other particles. It has been shown that by using the C-S model, the velocities of particles converge to a common value despite the absence of a central command. In this note, we make an extension of the C-S model by introducing additional interaction terms between agents which we refer to as the inter-particle bonding force, in order to incorporate collision avoidance between agents, and at the same time achieve tighter spatial configurations. The proposed inter-particle bonding force makes use of position and velocity information of other agents in order to achieve such separation and cohesion. With the inter-particle bonding forces and the velocity-alignment term of the original C-S model, we show the emergent behavior of asymptotic flocking to spatial equilibrium configurations.

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Target localization, whose goal is to estimate the location of an unknown target, is one of the key issues in applications of wireless sensor networks (WSNs). With recent advances in fabrication technology, deployments of large-scale WSNs have become economically feasible. However, there exist issues such as limited communication and the curse of dimensionality in applying machine-learning algorithms such as support vector regression (SVR) on large-scale WSNs. Here, in order to overcome such issues, we propose an ensemble implementation of SVR for the problem of target localization. The convergence property of the localization algorithm using the ensemble SVR is verified, and the robustness of the proposed scheme against measurement noise is analyzed. Furthermore, experimental results confirm that the estimation performance of the proposed method is more accurate and robust to measurement noise than the conventional SVR predictor.

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