One important way to extend the lifetime of wireless sensor networks (WSNs) is to manage the sleep scheduling of sensor nodes after they are deployed. Most of the existing works on node scheduling mainly concentrate on nodes which have only one sensor, and they regard a node and its sensor modules as a whole to manage sleep scheduling. Few works involve the sensed modules scheduling of the sensor nodes, which have multiple sensors. However, some of the sensed modules (such as video sensor) consume a lot of energy. Therefore, they have less energy efficiency for multisensory networks. In this paper, we propose a distributed and energy-balanced multisensory scheduling strategy (EBMS), which considers the scheduling of both the communication modules and the sensed modules for each node in target tracking WSNs. In EBMS, the network is organized as clustering structures. Each cluster head adaptively assigns a sleep time to its cluster members according to the position of the members. Energy-balanced multisensory scheduling strategy also proposes an energy balanced parameter to balance the energy consumption of each node in the network. In addition, multi-hop coordination scheme is proposed to find the optimal cooperation among clusters to maximize the energy conservation. Experimental results show that EBMS outperformed the state-of-the-art approaches.

In target tracking wireless sensor networks, choosing a part of sensor nodes to execute tracking tasks and letting the other nodes sleep to save energy are efficient node management strategies. However, at present more and more sensor nodes carry many different types of sensed modules, and the existing researches on node selection are mainly focused on sensor nodes with a single sensed module. Few works involved the management and selection of the sensed modules for sensor nodes which have several multi-mode sensed modules. This work proposes an efficient node and sensed module management strategy, called ENSMM, for multisensory WSNs (wireless sensor networks). ENSMM considers not only node selection, but also the selection of the sensed modules for each node, and then the power management of sensor nodes is performed according to the selection results. Moreover, a joint weighted information utility measurement is proposed to estimate the information utility of the multiple sensed modules in the different nodes. Through extensive and realistic experiments, the results show that, ENSMM outperforms the state-of-the-art approaches by decreasing the energy consumption and prolonging the network lifetime. Meanwhile, it reduces the computational complexity with guaranteeing the tracking accuracy.

为探索锗硅异质结双极晶体管(SiGe HBT)总剂量效应的损伤机理, 采用半导体器件三维模拟工具(TCAD), 建立电离辐照总剂量效应损伤模型, 分析比较电离辐射在SiGe HBT不同氧化层结构的不同位置引入陷阱电荷缺陷后, 器件正向Gummel特性和反向Gummel特性的退化特征, 获得SiGe HBT总剂量效应损伤规律, 并与⁶⁰Co γ辐照实验进行对比. 结果表明: 总剂量辐照在SiGe HBT器件中引入的氧化物陷阱正电荷主要在pn结附近的Si/SiO₂界面处产生影响, 引起pn结耗尽区的变化, 带来载流子复合增加, 最终导致基极电流增大、增益下降; 其中EB Spacer氧化层中产生的陷阱电荷主要影响正向Gummel特性, 而LOCOS隔离氧化层中的陷阱电荷则是造成反向Gummel特性退化的主要因素. 通过数值模拟分析获得的SiGe HBT总剂量效应损伤规律与不同偏置下⁶⁰Co γ辐照实验的结论符合得较好.

With the development of sensed technology, more and more sensor nodes carry multiple sensors in information collection wireless sensor networks. As a result, there are always a large number of correlated dynamic sensing data transmitted in the network. These data contain a lot of redundant information and errors, which leads to the resource waste and causes data congestion. Although various researches have focused on the sensing data collection and fusion, most of them do not consider the correlation of sensing data, and the network cannot adaptively collect data according to the accuracy required by users. Therefore, this article proposes a hierarchical data collection scheme for data-collecting wireless sensor networks. We combine the clustering and chain network structure and propose a probabilistic multi-mode sensing data selection method based on the characteristics of the sensors. Moreover, a data correlation analysis method based on gray correlation analysis is proposed to measure the similarity of the sensing data. Furthermore, we use the Bernoulli uniform sampling to estimate the approximate average value of data quality and make the approximation for the multi-mode sensing data on the basis of required data accuracy. Experimental results show the effectiveness of the proposed approach. And experiments prove that the proposed approach has better performance than state-of-the-art approaches.

Knowledge distillation serves as a prevalent model compression strategy within scene text detection, enabling the transfer of learned representations from a high-capacity teacher architecture to a streamlined student counterpart. Building upon this concept, deep mutual learning alleviates dependence on the teacher model through interactive learning among student models. However, existing deep mutual learning networks inadequately address the complex redundant backgrounds and text feature distributions in scene text images, failing to effectively balance the trade-off between model performance and lightweight design. To address this issue, this paper proposes an improved self-mutual learning framework based on deep mutual learning. By employing a design that incorporates parallel multi-detection heads and interactive learning, the proposed approach simplifies the model training process while significantly improving detection accuracy. Specifically, the framework introduces a pruning mechanism that enables different detection heads to capture input features with varying degrees of sparsity. This not only reduces interference from redundant backgrounds but also leads to a more lightweight implementation. Moreover, varying feature sparsity among detection heads promotes more diverse knowledge exchange throughout mutual learning. This substantially boosts the distilled model’s resilience in intricate text environments. Comprehensive evaluations show that our approach achieves superior F-measure scores compared to leading knowledge distillation methods.