Multicasting is an efficient technique for simultaneously transmitting common messages from the base station (BS) to multiple mobile users (MUs). Multicast scheduling over multiple channels, which aims to jointly minimize the energy consumption of the BS and the latency of serving asynchronized requests from the MUs, is formulated as an infinite-horizon Markov decision process (MDP) problem with a large discrete action space, multiple time-varying constraints, and multiple time-invariant constraints. To address these challenges, this paper proposes a novel distribution-embedding multi-agent proximal policy optimization (DE-MAPPO) algorithm, which consists of one modified MAPPO and one distribution-embedding module. The former one handles the large discrete action space and time-varying constraints by modifying the structure of the actor networks and the training kernel of the conventional MAPPO; and the latter one iteratively adjusts the action distribution to satisfy the time-invariant constraints. Moreover, a performance upper bound of the considered MDP is derived by solving a two-step optimization problem. Finally, numerical results demonstrate that our proposed algorithm outperforms the existing ones in terms of applicability, effectiveness, and robustness, and achieves comparable performance to the derived upper bound.

Pulse localization is the basic task of the pulse diagnosis with robot. More accurate location can reduce the misdiagnosis caused by different types of pulse. Traditional works usually use a collection surface with a certain area for contact detection, and move the collection surface to collect changes of power for pulse localization. These methods often require the subjects place their wrist in a given position. In this paper, we propose a novel pulse localization method which uses the infrared camera as the input sensor, and locates the pulse on wrist with the neural network. This method can not only reduce the contact between the machine and the subject, reduce the discomfort of the process, but also reduce the preparation time for the test, which can improve the detection efficiency. The experiments show that our proposed method can locate the pulse with high accuracy. And we have applied this method to pulse diagnosis robot for pulse data collection.

In this paper, we propose an end-to-end Attention-Block network for image retrieval (ABIR), which greatly increases the retrieval accuracy without human annotations like bounding boxes. Specifically, our network utilizes coarse-scale feature fusion, which generates the attentive local features via combining the information from different intermediate layers. Detailed feature information is extracted with the application of two attention blocks. Extensive experiments show that our method outperforms the state-of-the-art by a significant margin on four public datasets for image retrieval tasks.