Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

The traditional way of reading through Braille books is constraining the reading experience of blind or visually impaired (BVI) in the digital age. In order to improve the reading convenience of BVI, this paper proposes a low-cost and refreshable Braille display device, and solves the problems of high energy consumption and low latching force existing in existing devices. Further, the Braille display device was combined with the 3D Systems Touch device to develop an active Braille touch-reading system for digital reading of BVI with the help of the CHAI3D virtual environment. Firstly, according to the actual needs of BVI to touch and read the Braille dots, this paper utilizes the beam structure to provide a full latching function for the raised Braille dot without energy consumption. Through theoretical derivation and finite element analysis, the performance of the Braille dot actuator is optimized to provide sufficient feedback force and latching force for finger’s touch-reading. Then, this paper designs a virtual Braille interactive environment based on the CHAI3D, and combines the sense of touch with audio to effectively improve the recognition accuracy and reading efficiency of BVI for Braille through the multi-modal presentation of Braille information. The performance test results of the device show that the average lifting force of the Braille dot actuator is 101.67 mN, the latching force is over 5 N, and the average refresh frequency is 17.1 Hz, which meets the touch-reading needs of BVI. User experiments show that the average accuracy rate of BVI subjects in identifying digitized Braille is 95.5%, and subjects have a high subjective evaluation of the system.

Virtual Reality Motion Sickness (VRMS), which mainly caused by visual-vestibular-somatic conflict, is a risk to the health and security of users who have fun in virtual reality. To evaluate the VRMS in virtual reality experience, this paper introduces regression models based on recurrent neural network to predict the user’s VRMS severity from their EEG data. The EEG before, during and after a VRMS exposure task is collected and divided into 5 rhythms as the inputs. The Simulator Sickness Questionnaire (SSQ) is performed after each task to label the EEG data as the VRMS severity. In the proposed four regression models, four different approaches used to extract electrode-frequency features are combined with a three-layer Long Short-Term Memory (LSTM) or Bi-directional Long Short-Term Memory (BiLSTM) network used to study the temporal features. Besides, the different numbers of hidden units in LSTM and BiLSTM network are compared to choose an optimal one. The results suggest the fusion of all electrodes and rhythms information using convolutional neural network before inputting the LSTM/BiLSTM network provides the best regression performance, in which the mean MSE and R2-score are 53.6159 and 0.8683 respectively. The work introduced in this paper provides a method to assess the performance of VR productions and is an objective and direct guideline to overcome VRMS and optimize VR systems.

Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has three branches (a main module and two auxiliary modules, all of them are composed of convolutional neural networks (CNNs)), which focus on the overall information of bitemporal Google Earth image pairs, the changed areas and the unchanged areas, respectively. The proposed method is end-to-end trainable, and each component in the network does not need to be trained separately.

The good compliance of soft robots provides a reliable safety environment for human-robot interaction; however, it also creates challenges for adding sensors to soft robots. In this letter, we propose a self-sensing origami-inspired soft twisting actuator. The actuator is designed based on the structure of origami, enabling the compound motion of twist and contraction. The position sensor made from flexible fabric material is integrated in the soft actuator body. With twisting angle feedback, the self-sensing twisting actuator can not only provide expected motion but also acquire additional environment information based on real-time sensing. This letter discusses the design, fabrication, and experimental validation of proposed self-sensing twisting actuator. Based on the self-sensing twisting actuator, a soft gripper, a soft robotic arm, and a soft hexapod robot, all vacuum-powered, are designed and prototyped to validate their performance. The proposed soft actuator has great potential for applications in scenarios that require self-sensing information and closed-loop control.