Sleep scheduling is an effective mechanism to extend the lifetime of energy-constrained Wireless Sensor Networks(WSNs). It is often that the sensors are divided into sets with some constraints after plentiful sensors are deployed randomly, and then the sensors are scheduled to be activated successively according to the numbering of the sets. Many approaches divide the sensors into disjoint sets, which are not suitable for heterogeneous WSNs because of the waste of energy. In this paper, we propose a hybrid genetic algorithm which adopts greedy initialization and bidirectional mutation operations, termed BMHGA, to find a number of non-disjoint cover sets to prolong lifetime of heterogeneous WSNs, while subject to ensuring the full coverage of the monitoring area during the network lifetime. BMHGA adopts two-level structured chromosome to indicate the sensors and energy assignment to each set. A novel greedy method only uses little time to initialize the population that avoids time waste of random initialization. A new bidirectional mutation is proposed to keep multiplicity and global search. Through simulations, we show that the proposed algorithm outperforms the other existing approaches, finding the cover sets with longer lifetime by consuming less running time, especially in the large-scale networks. The experiment study also verifies the effectiveness of the proposed genetic operations and reveals the proper parameter settings for BMHGA.

In this study, we examine the cross‐view gait recognition problem. Many existing methods establish global feature representation based on the whole human body shape. However, they ignore some important details of different parts of the human body. In the latest literature, positioning partial regions to learn fine‐grained features has been verified to be effective in human identification. But they only consider coarse fine‐grained features and ignore the relationship between neighboring regions. Taken the above insights together, we propose a novel model called GaitGP, which learns both important details through fine‐grained features and the relationship between neighboring regions through global features. Our GaitGP model mainly consists of the following two aspects. First, we propose a Channel‐Attention Feature Extractor (CAFE) to extract the global features, which aggregates the channel‐level attention to enhance the spatial information in a novel convolutional component. Second, we present the Global and Partial Feature Combiner (GPFC) to learn different fine‐grained features, and combine them with the global features extracted by the CAFE to obtain the relevant information between neighboring regions. Experimental results on the CASIA gait recognition dataset B (CASIA‐B), The OU‐ISIR gait database, multi‐view large population dataset, and The OU‐ISIR gait database gait datasets show that our method is superior to the state‐of‐the‐art cross‐view gait recognition methods.

In order to alleviate the scale variation problem in object detection, many feature pyramid networks are developed. In this paper, we rethink the issues existing in current methods and design a more effective module for feature fusion, called multiflow feature fusion module (MF³M). We first construct gate modules and multiple information flows in MF³M to avoid information redundancy and enhance the completeness and accuracy of information transfer between feature maps. Furtherore, in order to reduce the discrepancy of classification and regression in object detection, a modified deformable convolution which is termed task adaptive convolution (TaConv) is proposed in this study. Different offsets and masks are predicted to achieve the disentanglement of features for classification and regression in TaConv. By integrating the above two designs, we build a novel feature pyramid network with feature fusion and disentanglement (FFAD) which can mitigate the scale misalignment and task misalignment simultaneously. Experimental results show that FFAD can boost the performance in most models.