Results 241 to 250 of about 157,621 (267)
Recapitulating Endochondral Ossification for Bone Repair: From Development to Engineering Strategy
Advanced Healthcare Materials, EarlyView.This review summarizes the developmental basis of endochondral ossification (ECO) and its applications in bone tissue engineering (BTE). It first outlines the key biological processes and signaling pathways underlying ECO, then discusses biomaterial‐based engineering strategies derived from these principles, and finally highlights future directions for
Yiqi Su +8 more
wiley +1 more source
Epidermal Patch Technologies for Integrated Healthcare and Infection Management
Advanced Healthcare Materials, EarlyView.Epidermal patches have evolved from simple wound coverings into multifunctional, skin‐conformable platforms integrating drug delivery, biosensing, and therapeutic functionalities. This review highlights their material innovations, fabrication strategies, and intelligent designs, including hydrogels, microneedles, and flexible electronics, while ...
Yuqi Wang +7 more
wiley +1 more source
Fully Wireless and Flexible Valves for Multiplexed and Prolonged Intravesical Liquid Release
Advanced Healthcare Materials, EarlyView.A fundamental mechanism for the simultaneous control of multiple magnetic valves is reported in a miniature soft robotic patch for intravesical drug delivery, enabling fully wireless and programmable liquid release. Integrated with a bioadhesive interface and induction‐coupling‐based liquid volume sensing, the soft robotic patch is promising to enable ...
Boyang Xiao +5 more
wiley +1 more source
Advanced Healthcare Materials, EarlyView.Gonzalez Martinez and collaborators develop a strategy to formulate high performance GelMA‐based bioinks with low solids contents. The resulting bioinks enable 3D bioprinting at 37 °C of high‐fidelity structures with tunable mechanical properties that support high cell viability and function.
David A. González‐Martínez +8 more
wiley +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
International Journal of Machine Learning and Cybernetics, 2021
The capsule network (CapsNet) is a novel network model that can learn spatial information in images. However, the performance of CapsNet on complex datasets (such as CIFAR10) is limited and it requires a large number of parameters. These disadvantages make CapsNet less useful, especially in some resource-constrained devices.
Kun Sun +3 more
openaire +1 more source
The capsule network (CapsNet) is a novel network model that can learn spatial information in images. However, the performance of CapsNet on complex datasets (such as CIFAR10) is limited and it requires a large number of parameters. These disadvantages make CapsNet less useful, especially in some resource-constrained devices.
Kun Sun +3 more
openaire +1 more source
CNN to Capsule Network Transformation
2020 Digital Image Computing: Techniques and Applications (DICTA), 2020Capsule Network has been recently proposed which outperforms CNN in specific tasks. Due to the network architecture differences between Capsule Network and CNN, Capsule Network could not use transfer learning which is very frequently used in CNN. In this paper, we propose a transfer learning method which can easily transfer CNN to Capsule Network.
Takumi Sato, Kazuhiro Hotta
openaire +1 more source
MapReduce-based Capsule Networks
2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), 2019Currently, artificial intelligence technology is attracting much attention, and image processing field is also making remarkable progress in recognition rate through CNN models. Furthermore, Capsule Network which is flexible in changing pose of image is being studied in various fields by improving disadvantage of Pooling Layer of CNN model.
Sun-Jin Park, Ho-Hyun Park
openaire +1 more source
A brief survey on Capsule Network
2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2020Capsule networks(CapsNets) are new kinds of network representation in deep learning. They are proposed to overcome the shortcomings of convolutional neural networks(CNNs). CNNs do not consider the important spatial level correlation between simple and complex objects. Besides, the pooling operations lose too much spatial information.
Ruiyang Shi, Lingfeng Niu
openaire +1 more source
2019
Multi-view learning attempts to generate a model with a better performance by exploiting information among multi-view data. Most existing approaches only focus on either consistency or complementarity principle, and learn representations (or features) of the multi-view data. In this paper, to utilize both complementarity and consistency simultaneously,
Jianwei Liu 0006 +5 more
openaire +1 more source
Multi-view learning attempts to generate a model with a better performance by exploiting information among multi-view data. Most existing approaches only focus on either consistency or complementarity principle, and learn representations (or features) of the multi-view data. In this paper, to utilize both complementarity and consistency simultaneously,
Jianwei Liu 0006 +5 more
openaire +1 more source