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In this paper, weighted average consensus of second-order multi-agent systems (MASs) via sampled-data control is investigated. Specially, since the intelligence degree of each agent may be different in practical applications, the concept of intelligence level is introduced to characterize the intelligence degrees of agents in this paper. A novel distributed continuous-time control protocol with various intelligence levels is put forward based on sampled data in order to reduce the updating rates and computational burden of the controller. A necessary and sufficient condition is derived to achieve weighted average consensus under the proposed protocol. Moreover, according to the acquired result, the authors also give the choosing approaches of the feedback gains and sampling period. Finally, several numerical simulations are provided to illustrate the reliability of the theoretical results. [ABSTRACT FROM AUTHOR]

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their limited capability in handling large heterogeneity of tracers and scanners of PET imaging. This study employs a simple way to integrate domain knowledge in DL for CT-free PET imaging. In contrast to conventional direct DL methods, we simplify the complex problem by a domain decomposition so that the learning of anatomy-dependent attenuation correction can be achieved robustly in a low-frequency domain while the original anatomy-independent high-frequency texture can be preserved during the processing. Even with the training from one tracer on one scanner, the effectiveness and robustness of our proposed approach are confirmed in tests of various external imaging tracers on different scanners. The robust, generalizable, and transparent DL development may enhance the potential of clinical translation. Deep learning-based methods have been proposed to substitute CT-based PET attenuation and scatter correction to achieve CT-free PET imaging. Here, the authors present a simple way to integrate domain knowledge in deep learning for CT-free PET imaging. [ABSTRACT FROM AUTHOR]

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