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The purpose of this study is to design a highly integrated smart glove to enable gesture acquisition and force sensory interactions, and to enhance the realism and immersion of virtual reality interaction experiences.

The smart glove is highly integrated with gesture sensing, force-haptic acquisition and virtual force feedback modules. Gesture sensing realizes the interactive display of hand posture. The force-haptic acquisition and virtual force feedback provide immersive force feedback to enhance the sense of presence and immersion of the virtual reality interaction.

The experimental results show that the average error of the finger bending sensor is only 0.176°, the error of the arm sensor is close to 0 and the maximum error of the force sensing is 2.08 g, which is able to accurately sense the hand posture and force-touch information. In the virtual reality interaction experiments, the force feedback has obvious level distinction, which can enhance the sense of presence and immersion during the interaction.

This paper innovatively proposes a highly integrated smart glove that cleverly integrates gesture acquisition, force-haptic acquisition and virtual force feedback. The glove enhances the sense of presence and immersion of virtual reality interaction through precise force feedback, which has great potential for application in virtual environment interaction in various fields.

Hyperspectral imaging (HSI) systems are becoming potent technologies for computer vision tasks due to the rich information they uncover, where each substance exhibits a distinct spectral distribution. Although the high spectral dimensionality of the data empowers feature learning, the joint spatial–spectral features have not been well explored yet. Gabor convolutional networks (GCNs) incorporate Gabor filters into a deep convolutional neural network (CNN) to extract discriminative features of different orientations and frequencies. To the best if the authors’ knowledge, this paper introduces the exploitation of GCNs for hyperspectral image classification (HSI-GCN) for the first time. HSI-GCN is able to extract deep joint spatial–spectral features more rapidly and accurately despite the shortage of training samples. The authors thoroughly evaluate the effectiveness of used method on different hyperspectral data sets, where promising results and high classification accuracy have been achieved compared to the previously proposed CNN-based and Gabor-based methods.

The authors have implemented the new algorithm of Gabor convolution network on the hyperspectral images for classification purposes.

Implementing the new GCN has shown unexpectable results with an excellent classification accuracy.

To the best of the authors’ knowledge, this work is the first one that implements this approach.