The hardness of rigid electronic devices limits its application scope. People use flexible scheme to improve it, so the design of flexible circuit structure is very important. Some special structures, such as island bridge structure, mesh structure and serpentine structure, can make the circuit have the ability of deformation. However, people cannot get good results from using a single structure, but the multi-level structures may improve the flexibility. Inspired by the softness of the net in our life, combined with the honeycomb structure, we design a 2-D honeycomb mesh structure. When the mesh is stretched, the 2-D hole is deformed by stress, and the local large strain converts to the small strain of the whole structure to avoid fracture. The stretch-ability of the single-level honeycomb mesh structure is 6.77%. Then, in order to further improve the flexibility, the serpentine structure is applied to the edge of the honeycomb structure to form a two-level structure. When the primary-level honeycomb structure is stretched, the second-level serpentine structure is also appropriately stretched to improve the flexibility. Finite element analysis shows the stretch-ability is 8.3%, which is better than single-level structure. Next, we also simulate the bending angle and twist angle of the structure, which has 120 degree bending (bending radius 1.55mm), 54 degree twisting and no plastic deformation occurs. It is clear that the multi-level microstructure has better flexibility, which provides a new scheme for the fabrication of flexible electronic devices and circuit microstructure design.
Nowadays many tasks such as medical correlation, surgery and so on almost depend on manpower. Because of occlusions and environmental impacts, artificial intelligence method can help little. For this situation, we design a multicamera stereo vision system which can be used in poor situation to catch and reconstruct certain object. To reduce the environmental impact, infrared devices like cameras, complement lamps and infrared reflection target balls are selected. The system reconstructs object through detecting target balls set on the key position of object instead of directly detect the whole object. When working, the self-adaptive system can choose all or part cameras due to occlusion and lighting conditions to capture pictures containing target spheres. To verify the feasibility and positioning accuracy of this system, we design a 4-DOF mechanical arm and arrange a set of experiments shown in this paper. The whole system can be applied as a real-time and precise auxiliary approach to many object detection tasks such as surgery navigation, position detection and object tracking.
Image segmentation plays an important role in medical science. One application is multimodality imaging, especially the fusion of structural imaging with functional imaging, which includes CT, MRI and new types of imaging technology such as optical imaging to obtain functional images. The fusion process require precisely extracted structural information, in order to register the image to it. Here we used image enhancement, morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in deep learning way. Such approach greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. The contours of the borders of different tissues on all images were accurately extracted and 3D visualized. This can be used in low-level light therapy and optical simulation software such as MCVM. We obtained a precise three-dimensional distribution of brain, which offered doctors and researchers quantitative volume data and detailed morphological characterization for personal precise medicine of Cerebral atrophy/expansion. We hope this technique can bring convenience to visualization medical and personalized medicine.
Image segmentation plays an important role in multimodality imaging, especially in fusion structural images offered by CT, MRI with functional images collected by optical technologies or other novel imaging technologies. Plus, image segmentation also provides detailed structure description for quantitative visualization of treating light distribution in the human body when incorporated with 3D light transport simulation method. Here we used image enhancement, operators, and morphometry methods to extract the accurate contours of different tissues such as skull, cerebrospinal fluid (CSF), grey matter (GM) and white matter (WM) on 5 fMRI head image datasets. Then we utilized convolutional neural network to realize automatic segmentation of images in a deep learning way. We also introduced parallel computing. Such approaches greatly reduced the processing time compared to manual and semi-automatic segmentation and is of great importance in improving speed and accuracy as more and more samples being learned. Our results can be used as a criteria when diagnosing diseases such as cerebral atrophy, which is caused by pathological changes in gray matter or white matter. We demonstrated the great potential of such image processing and deep leaning combined automatic tissue image segmentation in personalized medicine, especially in monitoring, and treatments.
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