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In the big data era, thousands of hundreds of devices play the role of data producer as well as data consumer. However, wireless devices bear the power exhausted problem in various situations. How to balance the trade-off between data processing speed and power efficiency is meaningful to be researched. In this study, we propose a Sleep Data Fusion Networks module (SFDN) which has a star topology Bluetooth network to fuse data of sleep-aware applications basing on our designed application protocol. In the network, a center Bluetooth device fuses data generated from target node Bluetooth devices. Due to the low power consumption of Bluetooth as well as connection safety, our method is a better choice for a long lifetime and non-real time sleep data processing and fusion tasks then the Wi-Fi-based approach used in EAST and Smart-Alarm sleep-aware applications.

With the maturing of Unmanned Aerial Vehicle (UAV) technology and the further expansion of aerial photography technology, China's civil unmanned aerial vehicle (UAV) application field is increasingly extensive. With disasters occurring suddenly, rescue forces are often unable to timely understand the situation and quickly arrive at the scene due to emergency rescue operations and poor rescue environment. UAV has the advantages of small size, low cost, flexibility, easy to use, low environmental requirements, which is suitable for war and disaster relief and other dangerous emergency environment. In this paper, a topology control algorithm based on k-hop local topology node mobility is proposed to realize the two-connected fault-tolerant network. The FTLMF algorithm proposed in this paper improves the success rate of implementing the two-connected fault-tolerant network under the constraint of node mobility, has a shorter adjustment period and offset distance, and maintain the connectivity of the original link. The topology control algorithm has a potential application prospect in network fault-tolerant control.

Aiming at the problems of existing in large-scale organizations, such as the disunity of message sending and receiving and management, the complexity and low efficiency of interconnection between departments and healthcare systems, this paper proposes the design of intelligent message collaboration system integrating multiple message interaction modes and questionnaire publishing. It realizes the message push mechanism which integrates SMS, email and instant communication, the feedback statistics mechanism based on questionnaire, and the performance and stability of the system are tested by providing a standard interface to connect with the business system. The experimental results show that the system is open, easy to expand and easy to maintain. It provides a solution for the unified release and supervision of information within large organizations such as hospital.

Inductor-integrated high-power high-frequency transformer can achieve soft switching of power semiconductor devices and reduce switching loss. That is widely used at high-voltage and high-power applications such as renewable energy grid-connected systems. The method of dividing a single air gap into several air gaps can effectively reduce the additional loss caused by the air gap. However, cutting the air gap in amorphous or nanocrystalline strips commonly used at large-capacity high-frequency applications will break the interlayer insulation and increase the eddy current loss at the core incision. Hence, the number of air gaps should be as few as possible. This paper introduces that three quasi-distributed air gaps can be arranged at three regions of the magnetic core according to the certain rule to effectively decrease winding loss. Finite element analyses (FEA) will be utilized to validate that the proposed method is better than that of the six quasi-distributed air gaps.

In this paper we propose a novel network module, namely Robust Attentional Pooling (RAP), that potentially can be applied in an arbitrary network for generating single vector representations for classification. By taking a feature matrix for each data sample as the input, our RAP learns data-dependent weights that are used to generate a vector through linear transformations of the feature matrix. We utilize feature selection to control the sparsity in weights for compressing the data matrices as well as enhancing the robustness of attentional pooling. As exemplary applications, we plug RAP into PointNet and ResNet for point cloud and image recognition, respectively. We demonstrate that our RAP significantly improves the recognition performance for both networks whenever sparsity is high. For instance, in extreme cases where only one feature per matrix is selected for recognition, RAP achieves more than 60% improvement over PointNet in terms of accuracy on the ModelNet40 dataset.