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Journal articles on the topic 'Capsnet'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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1

Deng, Fei, Shengliang Pu, Xuehong Chen, Yusheng Shi, Ting Yuan, and Shengyan Pu. "Hyperspectral Image Classification with Capsule Network Using Limited Training Samples." Sensors 18, no. 9 (2018): 3153. http://dx.doi.org/10.3390/s18093153.

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Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained

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2

C, Dr Ramya. "Comparative Performance Evaluation of VGG-16 and Capsnet using Kannada MNIST." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 1190–94. http://dx.doi.org/10.22214/ijraset.2021.37378.

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Abstract: Handwriting recognition is an important problem in character recognition. It is much more difficult especially for regional languages such as Kannada. In this regard there has been a recent surge of interest in designing convolutional neural networks (CNNs) for this problem. However, CNNs typically require large amounts of training data and cannot handle input transformations. Capsule networks, which is referred to as capsNets proposed recently to overcome these shortcomings and posed to revolutionize deep learning solutions. Our particular interest in this work is to recognize kanna

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3

Fentaw, Haftu Wedajo, and Tae-Hyong Kim. "Design and Investigation of Capsule Networks for Sentence Classification." Applied Sciences 9, no. 11 (2019): 2200. http://dx.doi.org/10.3390/app9112200.

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In recent years, convolutional neural networks (CNNs) have been used as an alternative to recurrent neural networks (RNNs) in text processing with promising results. In this paper, we investigated the newly introduced capsule networks (CapsNets), which are getting a lot of attention due to their great performance gains on image analysis more than CNNs, for sentence classification or sentiment analysis in some cases. The results of our experiment show that the proposed well-tuned CapsNet model can be a good, sometimes better and cheaper, substitute of models based on CNNs and RNNs used in sente

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4

Jia, Bohan, and Qiyu Huang. "DE-CapsNet: A Diverse Enhanced Capsule Network with Disperse Dynamic Routing." Applied Sciences 10, no. 3 (2020): 884. http://dx.doi.org/10.3390/app10030884.

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Capsule Network (CapsNet) is a methodology with good prospects in visual tasks, since it can keep a stronger relationship of spatial information than Convolutional Neural Networks (CNNs). However, the current Capsule Network do not provide performance as expected on several benchmark data sets with complex data and backgrounds. Inspired by the multiple capsules of Diverse Capsule Network (DCNet++) and the Spatial Group-wise Enhance (SGE) mechanism, we propose the Diverse Enhanced Capsule Network (DE-CapsNet), a hierarchical architecture which uses residual convolutional layers and the position

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Zhao, Guohua, Yaping Wu, Mengyang He, Jie Bai, Jingliang Cheng, and Yusong Lin. "Preprocessing and Grading of Glioma Data Acquired from Multicenter." Journal of Medical Imaging and Health Informatics 9, no. 6 (2019): 1236–45. http://dx.doi.org/10.1166/jmihi.2019.2724.

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Objectives: We developed a preprocessing flow for glioma multicenter data polymorphism and tested its effectiveness and availability through an improved ResNet network. We also explored if the current CapsNet model could be used for glioma grading. Materials and Methods: We used 276 patients with glioma and two datasets to generate 4556 brain images after multi-center pretreatment was performed and introduced them to the improved ResNet and CapsNet models, and analyzed the predicted results of the model. Results: After using the pretreatment procedure proposed in this study, we observed that t

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6

Zhang, Bolin, Jiangbo Qian, Xijiong Xie, Yu Xin, and Yihong Dong. "CapsNet-based supervised hashing." Applied Intelligence 51, no. 8 (2021): 5912–26. http://dx.doi.org/10.1007/s10489-020-02180-7.

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7

Wang, Duolin, Yanchun Liang, and Dong Xu. "Capsule network for protein post-translational modification site prediction." Bioinformatics 35, no. 14 (2018): 2386–94. http://dx.doi.org/10.1093/bioinformatics/bty977.

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Abstract Motivation Computational methods for protein post-translational modification (PTM) site prediction provide a useful approach for studying protein functions. The prediction accuracy of the existing methods has significant room for improvement. A recent deep-learning architecture, Capsule Network (CapsNet), which can characterize the internal hierarchical representation of input data, presents a great opportunity to solve this problem, especially using small training data. Results We proposed a CapsNet for predicting protein PTM sites, including phosphorylation, N-linked glycosylation,

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8

Al-Nuaimi, Dhamyaa H., Muhammad F. Akbar, Laith B. Salman, Intan S. Zainal Abidin, and Nor Ashidi Mat Isa. "AMC2N: Automatic Modulation Classification Using Feature Clustering-Based Two-Lane Capsule Networks." Electronics 10, no. 1 (2021): 76. http://dx.doi.org/10.3390/electronics10010076.

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The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer

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9

Zhou, Lijuan, Kai Feng, Hui Li, Shudong Zhang, and Xiangyang Huang. "Efficiency Optimization of Capsule Network Model Based on Vector Element." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (2020): 2052006. http://dx.doi.org/10.1142/s0218001420520060.

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Currently, Deep Learning and Convolutional Neural Network (CNN) have been widely used in many fields and have generated very high value in these fields, especially in the field of image recognition. But there are some deficiencies in certain issues of image recognition. For example, CNN’s recognizing performance is not good at different angles of objects and overlapping objects. Also, CNN is sometimes very sensitive to slight perturbations, modifying one pixel of a recognized image may cause recognition errors. For these problems, the capsule network (CapsNet) proposed by Geoffrey Hinton can s

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10

Cheng, Xinming, Jiangnan He, Jianbiao He, and Honglei Xu. "Cv-CapsNet: Complex-Valued Capsule Network." IEEE Access 7 (2019): 85492–99. http://dx.doi.org/10.1109/access.2019.2924548.

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11

Yang, Shuai, Feifei Lee, Ran Miao, et al. "RS-CapsNet: An Advanced Capsule Network." IEEE Access 8 (2020): 85007–18. http://dx.doi.org/10.1109/access.2020.2992655.

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12

Kumar, M., and V. E. Jayanthi. "Underdetermined blind source separation using CapsNet." Soft Computing 24, no. 12 (2019): 9011–19. http://dx.doi.org/10.1007/s00500-019-04430-4.

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13

Katebi, Reza, Yadi Zhou, Ryan Chornock, and Razvan Bunescu. "Galaxy morphology prediction using Capsule Networks." Monthly Notices of the Royal Astronomical Society 486, no. 2 (2019): 1539–47. http://dx.doi.org/10.1093/mnras/stz915.

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Abstract Understanding morphological types of galaxies is a key parameter for studying their formation and evolution. Neural networks that have been used previously for galaxy morphology classification have some disadvantages, such as not being inherently invariant under rotation. In this work, we studied the performance of Capsule Network (CapsNet), a recently introduced neural network architecture that is rotationally invariant and spatially aware, on the task of galaxy morphology classification. We designed two evaluation scenarios based on the answers from the question tree in the Galaxy Z

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14

Tampubolon, Yang, Chan, Sutrisno, and Hua. "Optimized CapsNet for Traffic Jam Speed Prediction Using Mobile Sensor Data under Urban Swarming Transportation." Sensors 19, no. 23 (2019): 5277. http://dx.doi.org/10.3390/s19235277.

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Urban swarming transportation (UST) is a type of road transportation where multiple types of vehicles such as cars, buses, trucks, motorcycles, and bicycles, as well as pedestrians are allowed and mixed together on the roads. Predicting the traffic jam speed under UST is very different and difficult from the single road network traffic prediction which has been commonly studied in the intelligent traffic system (ITS) research. In this research, the road network wide (RNW) traffic prediction which predicts traffic jam speeds of multiple roads at once by utilizing citizens’ mobile GPS sensor rec

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15

Zhang, Wei, Ping Tang, and Lijun Zhao. "Remote Sensing Image Scene Classification Using CNN-CapsNet." Remote Sensing 11, no. 5 (2019): 494. http://dx.doi.org/10.3390/rs11050494.

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Remote sensing image scene classification is one of the most challenging problems in understanding high-resolution remote sensing images. Deep learning techniques, especially the convolutional neural network (CNN), have improved the performance of remote sensing image scene classification due to the powerful perspective of feature learning and reasoning. However, several fully connected layers are always added to the end of CNN models, which is not efficient in capturing the hierarchical structure of the entities in the images and does not fully consider the spatial information that is importa

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Mensah, Patrick Kwabena, Benjamin Asubam Weyori, and Mighty Abra Ayidzoe. "Capsule network with K-Means routingfor plant disease recognition." Journal of Intelligent & Fuzzy Systems 40, no. 1 (2021): 1025–36. http://dx.doi.org/10.3233/jifs-201226.

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Capsule Networks (CapsNets) excel on simple image recognition problems. However, they fail to perform on complex images with high similarity and background objects. This paper proposes Local Binary Pattern (LBP) k-means routing and evaluates its performance on three publicly available plant disease datasets containing images with high similarity and background objects. The proposed routing algorithm adopts the squared Euclidean distance, sigmoid function, and a ‘simple-squash’ in place of dot product, SoftMax normalizer, and the squashing function found respectively in the dynamic routing algo

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17

Berman, Daniel S. "DGA CapsNet: 1D Application of Capsule Networks to DGA Detection." Information 10, no. 5 (2019): 157. http://dx.doi.org/10.3390/info10050157.

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Domain generation algorithms (DGAs) represent a class of malware used to generate large numbers of new domain names to achieve command-and-control (C2) communication between the malware program and its C2 server to avoid detection by cybersecurity measures. Deep learning has proven successful in serving as a mechanism to implement real-time DGA detection, specifically through the use of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This paper compares several state-of-the-art deep-learning implementations of DGA detection found in the literature with two novel mode

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18

Jiang, Xuefeng, Yikun Wang, Wenbo Liu, Shuying Li, and Junrui Liu. "CapsNet, CNN, FCN: Comparative Performance Evaluation for Image Classification." International Journal of Machine Learning and Computing 9, no. 6 (2019): 840–48. http://dx.doi.org/10.18178/ijmlc.2019.9.6.881.

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19

Chao, Hao, Liang Dong, Yongli Liu, and Baoyun Lu. "Emotion Recognition from Multiband EEG Signals Using CapsNet." Sensors 19, no. 9 (2019): 2212. http://dx.doi.org/10.3390/s19092212.

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Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced

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20

Xiang, Canqun, Lu Zhang, Yi Tang, Wenbin Zou, and Chen Xu. "MS-CapsNet: A Novel Multi-Scale Capsule Network." IEEE Signal Processing Letters 25, no. 12 (2018): 1850–54. http://dx.doi.org/10.1109/lsp.2018.2873892.

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21

Zhong, Xian, Jinhang Liu, Lin Li, et al. "An emotion classification algorithm based on SPT-CapsNet." Neural Computing and Applications 32, no. 7 (2019): 1823–37. http://dx.doi.org/10.1007/s00521-019-04621-y.

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22

T., Dr Vijayakumar, and Mr Vinothkanna R. "Capsule Network on Font Style Classification." June 2020 2, no. 2 (2020): 64–76. http://dx.doi.org/10.36548/jaicn.2020.2.001.

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Verification of font style followed in a file is a difficult task to classify. An artificial intelligence based algorithm network can effectively perform this task in reduced time. Capsule network is one among such algorithm and an emerging technique implemented for so many classification process with limited datasets. The proposed font style classification algorithm is enforced with Capsule Network (CapsNet) algorithm for executing the font style classification task. The proposed method is confirmed by classifying times new roman, Arial black and Algerian font style in English letters along w

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23

Zhu, W., W. Tan, L. Ma, D. Zhang, J. Li, and M. A. Chapman. "A CAPSNETS APPROACH TO PAVEMENT CRACK DETECTION USING MOBILE LASER SCANNNING POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2021 (June 28, 2021): 39–44. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2021-39-2021.

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Abstract. Routine pavement inspection is crucial to keep roads safe and reduce traffic accidents. However, traditional practices in pavement inspection are labour-intensive and time-consuming. Mobile laser scanning (MLS) has proven a rapid way for collecting a large number of highly dense point clouds covering roadway surfaces. Handling a huge amount of unstructured point clouds is still a very challenging task. In this paper, we propose an effective approach for pavement crack detection using MLS point clouds. Road surface points are first converted into intensity images to improve processing

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Wang, Chunyuan, Yang Wu, Yihan Wang, and Yiping Chen. "Scene Recognition Using Deep Softpool Capsule Network Based on Residual Diverse Branch Block." Sensors 21, no. 16 (2021): 5575. http://dx.doi.org/10.3390/s21165575.

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With the improvement of the quality and resolution of remote sensing (RS) images, scene recognition tasks have played an important role in the RS community. However, due to the special bird’s eye view image acquisition mode of imaging sensors, it is still challenging to construct a discriminate representation of diverse and complex scenes to improve RS image recognition performance. Capsule networks that can learn the spatial relationship between the features in an image has a good image classification performance. However, the original capsule network is not suitable for images with a complex

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Wang, Xue, Kun Tan, Qian Du, Yu Chen, and Peijun Du. "Caps-TripleGAN: GAN-Assisted CapsNet for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing 57, no. 9 (2019): 7232–45. http://dx.doi.org/10.1109/tgrs.2019.2912468.

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26

Sungheetha, Dr Akey, and Dr Rajesh Sharma R. "A Novel CapsNet based Image Reconstruction and Regression Analysis." Journal of Innovative Image Processing 2, no. 3 (2020): 156–64. http://dx.doi.org/10.36548/jiip.2020.3.006.

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In the field of image processing, all types of computation models are almost evolved to solve the issues through encoded neurons. However, compared with decoding orientation and regression analysis, still the doors are open due to its complexity. At present technologies uses two steps such as, decoding the intermediate terms and reconstruction using decoded information. The performance in terms of regression analysis is lagging due to the decoded intermediate terms. Conventional neural network models perform better in feature classification and representation, though the performance is reduced

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Jiang, Xuefeng, Yue Zhang, Wenbo Liu, et al. "Hyperspectral Image Classification With CapsNet and Markov Random Fields." IEEE Access 8 (2020): 191956–68. http://dx.doi.org/10.1109/access.2020.3029174.

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BIAN, Jianpeng, Jiaxing HAO, Shuai ZHAO, Weijing HUA, and Shichuang GAO. "Fault identification of catenary dropper based on improved CapsNet." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 3 (2020): 1491–502. http://dx.doi.org/10.3906/elk-1908-138.

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Kim, Minjong, and Suyoung Chi. "Detection of centerline crossing in abnormal driving using CapsNet." Journal of Supercomputing 75, no. 1 (2018): 189–96. http://dx.doi.org/10.1007/s11227-018-2459-6.

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30

Wang, Aili, Minhui Wang, Haibin Wu, Kaiyuan Jiang, and Yuji Iwahori. "A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet." Sensors 20, no. 4 (2020): 1151. http://dx.doi.org/10.3390/s20041151.

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LiDAR data contain feature information such as the height and shape of the ground target and play an important role for land classification. The effect of convolutional neural network (CNN) for feature extraction on LiDAR data is very significant, however CNN cannot resolve the spatial relationship of features adequately. The capsule network (CapsNet) can identify the spatial variations of features and is widely used in supervised learning. In this article, the CapsNet is combined with the residual network (ResNet) to design a deep network-ResCapNet for improving the accuracy of LiDAR classifi

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Ha, Kwon-Woo, and Jin-Woo Jeong. "Motor Imagery EEG Classification Using Capsule Networks." Sensors 19, no. 13 (2019): 2854. http://dx.doi.org/10.3390/s19132854.

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Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust perfo

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Mittal, Ansh, Deepika Kumar, Mamta Mittal, et al. "Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images." Sensors 20, no. 4 (2020): 1068. http://dx.doi.org/10.3390/s20041068.

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An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had

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Maity, Maitreya, Ayush Jaiswal, Kripasindhu Gantait, Jyotirmoy Chatterjee, and Anirban Mukherjee. "Quantification of malaria parasitaemia using trainable semantic segmentation and capsnet." Pattern Recognition Letters 138 (October 2020): 88–94. http://dx.doi.org/10.1016/j.patrec.2020.07.002.

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Yin, Jihao, Sen Li, Hongmei Zhu, and Xiaoyan Luo. "Hyperspectral Image Classification Using CapsNet With Well-Initialized Shallow Layers." IEEE Geoscience and Remote Sensing Letters 16, no. 7 (2019): 1095–99. http://dx.doi.org/10.1109/lgrs.2019.2891076.

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Wang, Hao, Kun Shao, and Xing Huo. "An improved CapsNet applied to recognition of 3D vertebral images." Applied Intelligence 50, no. 10 (2020): 3276–90. http://dx.doi.org/10.1007/s10489-020-01695-3.

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Çayır, Aykut, Uğur Ünal, and Hasan Dağ. "Random CapsNet forest model for imbalanced malware type classification task." Computers & Security 102 (March 2021): 102133. http://dx.doi.org/10.1016/j.cose.2020.102133.

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Lin, Zhongqi, Wanlin Gao, Jingdun Jia, and Feng Huang. "CapsNet meets SIFT: A robust framework for distorted target categorization." Neurocomputing 464 (November 2021): 290–316. http://dx.doi.org/10.1016/j.neucom.2021.08.087.

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Jampour, Mahdi, Saeid Abbaasi, and Malihe Javidi. "CapsNet regularization and its conjugation with ResNet for signature identification." Pattern Recognition 120 (December 2021): 107851. http://dx.doi.org/10.1016/j.patcog.2021.107851.

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Zhao, Xianping, Zhenyu Gao, and Gang Wen. "Remote sensing image-based wildfire recognition using capsnet for transmission lines." IOP Conference Series: Earth and Environmental Science 513 (July 8, 2020): 012072. http://dx.doi.org/10.1088/1755-1315/513/1/012072.

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40

Goceri, Evgin. "CapsNet topology to classify tumours from brain images and comparative evaluation." IET Image Processing 14, no. 5 (2020): 882–89. http://dx.doi.org/10.1049/iet-ipr.2019.0312.

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Li, Yuancheng, and Shanshan Yang. "GPS Spoofing attack detection in smart grids based on improved CapsNet." China Communications 18, no. 3 (2021): 174–86. http://dx.doi.org/10.23919/jcc.2021.03.014.

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Chen, Guoqi, Wanliang Wang, Zheng Wang, Honghai Liu, Zelin Zang, and Weikun Li. "Two-dimensional discrete feature based spatial attention CapsNet For sEMG signal recognition." Applied Intelligence 50, no. 10 (2020): 3503–20. http://dx.doi.org/10.1007/s10489-020-01725-0.

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Zhang, Xiaoqing, and Shuguang Zhao. "Blood Cell Image Classification Based on Image Segmentation Preprocessing and CapsNet Network Model." Journal of Medical Imaging and Health Informatics 9, no. 1 (2019): 159–66. http://dx.doi.org/10.1166/jmihi.2019.2555.

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Zhang, XiaoQing, and Shu-Guang Zhao. "Cervical image classification based on image segmentation preprocessing and a CapsNet network model." International Journal of Imaging Systems and Technology 29, no. 1 (2018): 19–28. http://dx.doi.org/10.1002/ima.22291.

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Biswal, B., Geetha Pavani P, Prasanna T, and Prakash Kumar karn. "Robust segmentation of exudates from retinal surface using M-CapsNet via EM routing." Biomedical Signal Processing and Control 68 (July 2021): 102770. http://dx.doi.org/10.1016/j.bspc.2021.102770.

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Xiang, Huiling, Yao-Sian Huang, Chu-Hsuan Lee, et al. "3-D Res-CapsNet convolutional neural network on automated breast ultrasound tumor diagnosis." European Journal of Radiology 138 (May 2021): 109608. http://dx.doi.org/10.1016/j.ejrad.2021.109608.

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Cejudo, Jose E., Akhilanand Chaurasia, Ben Feldberg, Joachim Krois, and Falk Schwendicke. "Classification of Dental Radiographs Using Deep Learning." Journal of Clinical Medicine 10, no. 7 (2021): 1496. http://dx.doi.org/10.3390/jcm10071496.

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Objectives: To retrospectively assess radiographic data and to prospectively classify radiographs (namely, panoramic, bitewing, periapical, and cephalometric images), we compared three deep learning architectures for their classification performance. Methods: Our dataset consisted of 31,288 panoramic, 43,598 periapical, 14,326 bitewing, and 1176 cephalometric radiographs from two centers (Berlin/Germany; Lucknow/India). For a subset of images L (32,381 images), image classifications were available and manually validated by an expert. The remaining subset of images U was iteratively annotated u

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Zhang, XiaoQing, and Shu-Guang Zhao. "Fluorescence microscopy image classification of 2D HeLa cells based on the CapsNet neural network." Medical & Biological Engineering & Computing 57, no. 6 (2019): 1187–98. http://dx.doi.org/10.1007/s11517-018-01946-z.

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Torrents-Barrena, Jordina, Gemma Piella, Eduard Gratacos, Elisenda Eixarch, Mario Ceresa, and Miguel A. Gonalez Ballester. "Deep Q-CapsNet Reinforcement Learning Framework for Intrauterine Cavity Segmentation in TTTS Fetal Surgery Planning." IEEE Transactions on Medical Imaging 39, no. 10 (2020): 3113–24. http://dx.doi.org/10.1109/tmi.2020.2987981.

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Piciarelli, Claudio, Pankaj Mishra, and Gian Luca Foresti. "Supervised Anomaly Detection with Highly Imbalanced Datasets Using Capsule Networks." International Journal of Pattern Recognition and Artificial Intelligence 35, no. 08 (2021): 2152010. http://dx.doi.org/10.1142/s0218001421520108.

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Detecting anomalous patterns in data is a relevant task in many practical applications, such as defective items detection in industrial inspection systems, cancer identification in medical images, or attacker detection in network intrusion detection systems. This paper focuses on detection of anomalous images, this is images that visually deviate from a reference set of regular data. While anomaly detection has been widely studied in the context of classical machine learning, the application of modern deep learning techniques in this field is still limited. We here propose a capsule-based netw

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