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This paper proposes a novel importance-based persistent coverage for environments with hotspots. Multiple quadrotors are utilized to maintain coverage while acquiring high-quality information over prioritized regions by lowering the altitude. To achieve this, the approach utilizes distributed communication protocols and cost functions to address the tradeoff between wider coverage and better quality information. The cost functions determine three-dimensional waypoints based on coverage levels, sensor resolution, and Euclidean distance. Voronoi tessellations are used locally for efficient computation and collision avoidance. Trajectories of the quadrotors are generated based on quadrotor dynamics using error functions that take into account state information and desired waypoints. The proposed approach is validated through simulations, comparisons, and experiments for multi-agent systems under quadrotor dynamics. Further, metrics of performance analysis are formulated that show consistent coverage levels and differences to assess the novel importance-based approach. The simulations and experiments demonstrate the feasibility and effectiveness of the approach, with prioritized regions receiving higher sensor resolution while maintaining persistent coverage of the environment. • Novel importance-based persistent coverage for information acquisition over hotspots. • Quadrotor waypoints based on information resolution and decaying environmental costs. • Distributed communication protocols for scalability and low computational complexity. • Real-time importance-based persistent coverage for multi-quadrotor dynamical systems. • Proposed coverage with varying velocities is verified by simulation and experiments. [ABSTRACT FROM AUTHOR]

In this paper, we consider a distributed economic dispatch problem (EDP) in smart grids, where each generator only communicates with its neighbors and minimizes its own cost in the presence of network-wide equality constraints and local capacity limits. Distributed algorithms to solve this problem have become an attractive focus of engineering research due to their multiple advantages. Many existing methods are time-triggered, whereas few works have been dedicated to solving the problem using an event-triggered approach. This problem gets more challenging when further accelerated convergence is desired. To solve such a problem, we carefully design an event-triggered parameter distributed accelerated algorithm, named as ET-PDA. On the one hand, the choice of different parameters in ET-PDA will lead to different momentum (Nesterov or heavy-ball) methods, which facilitates the accelerated convergence of the algorithm. On the other hand, the event-triggered mechanism in ET-PDA makes the time gap between two continuous interaction moments of each generator larger than the iteration interval, contributing to improved communication efficiency. In particular, ET-PDA achieves linear convergence when the local cost function of each generator is smooth and strongly convex, moreover, it precludes Zeno-like behavior. The effectiveness of ET-PDA is also verified through a series of simulation experiments. [ABSTRACT FROM AUTHOR]

As a blind equalization algorithm, the fractional lower-order statistics based multimodulus algorithm (FLOS-MMA) is suitable for equalizing multilevel quadrature amplitude modulation (QAM) signals in impulsive noise. However, the equalization performance of FLOS-MMA is severely degraded in strong impulsive noise because the first-order gradient of the cost function constrains the ability of FLOS to suppress large outliers in the equalizer output signal and is sensitive to large outliers in the equalizer input signal. To enhance the robustness of multimodulus blind equalization algorithm against impulsive noise, a FLOS based momentum fractional-order multimodulus algorithm (FLOS-MFOMMA) is proposed in this paper. The proposed algorithm mitigates the adverse effects of impulsive noise on the updating of equalizer coefficients by utilizing the fractional-order gradient and accelerates the convergence speed by incorporating a momentum term. Then, the steady-state excess mean square error and the computational complexity of FLOS-MFOMMA are analyzed theoretically. Finally, numerical simulations, for equalizing 16-QAM signals under impulsive noise environments, show that the proposed algorithm is superior to FLOS-MMA in terms of robustness and convergence speed. [ABSTRACT FROM AUTHOR]