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Wireless sensor networks (WSNs) for condition monitoring of mechanical equipment have been shown effective for ensuring operational safety and reducing breakdown losses. For battery-supply sensor nodes, energy efficiency for data transmission of mass vibration signals has become a significant challenge. In this article, an efficient low redundant data method is designed and implemented in the WSN nodes for reliable transmission. The proposed method includes two stages. Stage 1 involves the delta-discrete cosine transform (delta-DCT) that reduces the temporal and frequency redundancy of the original data so that the transferred data can be downsized via range coding. In stage 2, the sparse random reconstruction is injected to increase the redundancy of the compressed data to guarantee reliable transmission. Experimental results demonstrate that the original data can be downsized to 50%–55%, and the compression data can be recovered via sparse reconstruction without any data loss. [ABSTRACT FROM AUTHOR]

Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy. Despite that variants of U-Net are state-of-the-art segmentation models, they have limited performance when dealing with ABCs structures with various shapes and sizes, especially thin structures (e.g., the falx cerebri) that span only few slices. To deal with this problem, we propose a High and Multi-Resolution Network (HMRNet) that consists of a multi-scale feature learning branch and a high-resolution branch, which can maintain the high-resolution contextual information and extract more robust representations of anatomical structures with various scales. We further design a Bidirectional Feature Calibration (BFC) block to enable the two branches to generate spatial attention maps for mutual feature calibration. Considering the different sizes and positions of ABCs structures, our network was applied after a rough localization of each structure to obtain fine segmentation results. Experiments on the MICCAI 2020 ABCs challenge dataset showed that: 1) Our proposed two-stage segmentation strategy largely outperformed methods segmenting all the structures in just one stage; 2) The proposed HMRNet with two branches can maintain high-resolution representations and is effective to improve the performance on thin structures; 3) The proposed BFC block outperformed existing attention methods using monodirectional feature calibration. Our method won the second place of ABCs 2020 challenge and has a potential for more accurate and reasonable delineation of CTV of brain tumors. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) for mechanical vibration monitoring has been proposed for harsh environment monitoring purposes. However, links congestion makes the realization of reliable and energy-efficient transmission a challenge for such WSNs. To ease bandwidth pressure, a beacon synchronization-based multi-channel with dynamic time slot assignment method is proposed in this paper. Firstly, the network is separated into two opposing groups based on working state to maximize transmission performance. Secondly, the channel is allocated based on interference minimization from adjacent channels. To ensure the two opposing groups work individually, the nodes are synchronously triggered by transmission beacon, then nodes backoff and delay for a period of time to transmit data. Moreover, a dynamic timeslot assignment method is proposed to decrease channel collision. Finally, the experimental results demonstrate that the proposed methods can effectively schedule the network for data transmission via multi-channel, and the channel utilization performs better than CSMA/CA and TDMA. [ABSTRACT FROM AUTHOR]

This article investigates the optimal synchronization problem for unknown discrete-time nonlinear heterogeneous multiagent systems (MASs). It is very intractable to derive the analytical solutions of coupled Bellman’s equations, which are necessary to overcome this problem. We propose a data-based optimal synchronization control strategy based on a hierarchical and distributed optimal control framework composed of a model reference adaptive control (MRAC) layer and a distributed control layer. In the MRAC layer, the similar-offline MRAC algorithm is developed to make subsystems of MASs track their reference models, respectively. Then, the distributed optimal control problem of nonlinear heterogeneous MASs is transformed into that of homogeneous MASs composed of the reference models and the leader. In the distributed control layer, the distributed reference policy iteration algorithm is proposed to derive the solutions of coupled composite nonlinear Bellman’s equations, which ensure that the homogeneous MASs reach synchronization with optimum. The suboptimal synchronization control is achieved via optimization further. Convergence analysis of both algorithms is rigorously provided. The simulation results verify the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Short-term road traffic speed prediction is a long-standing topic in the area of Intelligent Transportation System. Apparently, effective prediction of the traffic speed on the road can not only provide timely details for the navigation system concerned and help the drivers to make better path selection, but also greatly improve the road supervision efficiency of the traffic department. At present, some researches on speed prediction based on GPS data, by adding weather and other auxiliary information, using graph convolutional neural network to capture the temporal and spatial characteristics, have achieved excellent results. In this paper, the problem of short-term traffic speed prediction based on GPS positioning data is further studied. For the processing of time series, we innovatively introduce Dynamic Time Warping algorithm into the problem and propose a Long Short-Term Memory with Dynamic Time Warping (D-LSTM) model. D-LSTM model, which integrates Dynamic Time Warping algorithm, can fine-tune the time feature, thus adjusting the current data distribution to be close to the historical data. More importantly, the fine-tuned data can still get a distinct improvement without special treatment of holidays. Meanwhile, considering that the data under different feature distributions have different effects on the prediction results, attention mechanism is also introduced in the model. Our experiments show that our proposed model D-LSTM performs better than other basic models in many kinds of traffic speed prediction problems with different time intervals, and especially significant in the traffic speed prediction on weekends. [ABSTRACT FROM AUTHOR]