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Pulse coupled neural network (PCNN) is a kind of visual cortex-inspired biological neural network, which has been proved a powerful candidate in the field of digital image processing due to its unique characteristics of global coupling and pulse synchronization. Notably, the inherent parameters estimation issue emerging in the entire system greatly affects the overall network performance. In this paper, a novel memristor crossbar array with its corresponding peripheral circuits is proposed, which is able to construct a general memristor-based PCNN (MPCNN) with variable linking coefficient. In order to verify the effectiveness and generality of the presented network, the single-channel MPCNN is further applied into the multi-focus image fusion problem with an improved multi-channel configuration. Correspondingly, a new type of MPCNN-based image fusion algorithm is put forward along with the design of an appropriate mapping function based on the image orientation information measure. Finally, a series of contrast experiments with comprehensive analysis demonstrate that the proposed fusion method has superior performances in terms of image quality and fusion effect compared to several existing algorithms. [ABSTRACT FROM AUTHOR]

Reconfigurable nonvolatile logic within the crossbar array provides a promising approach for constructing a non-von Neumann architecture that integrates computation and storage. In this paper, a nonvolatile Boolean logic configuration based on one-transistor-one-memristor (1T1M) cells is presented. The proposed logic configuration is able to execute diverse Boolean logic operations simply by altering the applied voltage level. The logic design can be mapped to the 1T1M array, and the operation results can be stored in situ. Because of the high flexibility of the logic configuration, the 1T1M array can be configured to perform the encryption and decryption of binary images. The 1T1M array can be also used to realize composite logic functions. Furthermore, the impact of resistance variation on logic operations is taken into consideration, showing strong robustness. Simulation results demonstrate the feasibility of the proposed reconfigurable nonvolatile logic design. [ABSTRACT FROM AUTHOR]

Multi-source domain adaptation, an effective solution for rotating machinery fault diagnosis, has achieved great success. However, previous multi-source domain adaptation based methods typically achieve domain consistency by aligning the global distribution of the source and target domains, regardless of private domain information and sufficient feature transferability, thereby limiting the diagnosis performance in cross-domain diagnostic scenarios. To address these issues, we propose a novel fine-grained feature decoupling based multi-source domain adaptation network (FD-MDAN) for fault diagnosis. Leveraging fine-grained feature decoupling, our FD-MDAN weakens specific features with private domain information and reinforces invariant features with diverse transferability at both of domain and category levels. We further develop a diversity regular term to minimize the feature redundancy, enhancing the transferability. In addition, to ensure that these decoupled features retain representative information of the original data, a reconstruction module is designed to make the reconstructed data and the original data consistent. Extensive experiments are conducted on three rotating machinery datasets. Benefiting from these components, our proposed FD-MDAN demonstrates state-of-the-art performance on various transfer diagnosis tasks. • A novel FD-MDAN is proposed for rotating machinery fault diagnosis under variable operating conditions. • Diverse multi-domain transferability is greatly enhanced for better generalization. • A fine-grained feature decoupling module is proposed for alleviating domain distribution shifts. • A diversity regular term is designed to minimize the feature redundancy. • Excellent performance in complex diagnosis scenarios is verified. [ABSTRACT FROM AUTHOR]

• A progressive multi-source domain adaptation method is proposed for bearing fault diagnosis. • Source-source and source-target domain pairs are mapped to different feature spaces for domain pairwise alignment by LMMD metric. • Category confusion constraint is applied to reduce ambiguous samples. • Through a progressive optimisation process, domain-uninformative and category-informative features are guided. Based on massive samples collected from various working conditions, multi-source domain adaptation-based fault diagnosis methods have been a promising way to improve the generalization capability of diagnostic models. However, insufficient multi-domain alignment and category confusion issues lead to unsatisfactory diagnostic performance in practical cross-domain scenarios. This article proposes a progressive multi-source domain adaptation method, which learns domain-uninformative and category-informative features. First, all domains are divided into multiple source-source and source-target domain pairs on different feature spaces to explore rich domain-invariant features, where local maximum mean discrepancy is introduced for domain pairwise alignment. Second, category correlation is reduced to allow ambiguous samples to obtain clear category boundaries. Extensive experimental results show the outstanding performance of our method. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency and reduce the labor cost of cafeterias, an intelligent master–slave collaborative robot system is developed for cafeteria service in this paper. The developed system can automatically complete the tasks of scooping dishes, taking bowls and pouring dishes into the bowl based on master–slave collaboration. Specifically, a dynamic geometry feature graph convolution network (DGG) is devised using the 3D point cloud of the dish, which can efficiently predict the scooping positions of the different dishes. Moreover, a master–slave motion planning control method is proposed to achieve fast and smooth trajectories for both arms, which can accomplish the cafeteria service tasks collaboratively. Furthermore, we establish a dataset containing point clouds and color images of various Chinese food. Experiments demonstrate that the DGG network can achieve superior performance over other state-of-the-art point cloud segmentation networks. Besides, the designed robot system can well meet the requirements of operation accuracy and speed, confirming its practicality in cafeteria services. • An intelligent master–slave collaborative robot system is developed for cafeteria service. • A novel point cloud segmentation network DGG is devised to predict the scooping positions of different dishes. • Using the master–slave motion planning, the proposed system can realize efficient and close coordination. [ABSTRACT FROM AUTHOR]