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1. Mobile Breast Cancer e-Support Program for Chinese Women With Breast Cancer Undergoing Chemotherapy (Part 3): Secondary Data Analysis

Author

Zhu, Haihua, Chen, Xiuwan, Yang, Jinqiu, Wu, Qiaoling, Zhu, Jiemin, and Chan, Sally Wai-Chi

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundMany app-based interventions targeting women with breast cancer have been developed and tested for effectiveness. However, information regarding the evaluation of the usage of these interventions is scarce. A better understanding of usage data is important to determine how women use apps and how these interventions affect health outcomes. ObjectiveThis study aimed to examine the usage duration and login frequency of an app-based intervention, the Breast Cancer e-Support (BCS) program, and to investigate the association between usage data and participants’ demographic and medical characteristics. MethodsThis study is a secondary data analysis of a randomized controlled trial assessing the effectiveness of the BCS program. The BCS program contains four modules: Learning Forum, Discussion Forum, Ask-the-Expert Forum, and Your Story Forum. A total of 57 women in the intervention group accessed the BCS program during their 12-week chemotherapy. The app’s background system tracked the usage duration and login frequency for each forum and the entire BCS program. ResultsThe total usage duration per participant ranged from 0 to 9371 minutes, and the login frequency per participant ranged from 0 to 774 times. The Discussion Forum and the Learning Forum were the most frequently used modules. The general linear model showed that age, education, family monthly income, and employment were associated with BCS usage duration and/or login frequency. Age (F1,45=10.09, P=.003, B=115.34, 95% CI 42.22-188.47) and education level (F1,45=7.22, P=.01, B=1949.63, 95% CI 487.76-3411.50) were positively associated with the usage duration of the entire BCS program. Family monthly income was positively associated with the usage duration of the Learning Forum (F1,45=11.85, P=.001, B=1488.55, 95% CI 617.58-2359.51) and the login frequency of the entire BCS program (F1,45=4.47, P=.04, B=113.68, 95% CI 5.33-222.03). Employment was negatively associated with the usage duration of the Ask-the-expert Forum (F1,45=4.50, P=.04, B=–971.87, 95% CI –1894.66 to –49.07) and the Your Story Forum (F1,45=5.36, P=.03, B=–640.71, 95% CI –1198.30 to –83.11) and positively associated with the login frequency of the entire BCS program (F1,45=10.86, P=.002, B=192.88, 95% CI 75.01-310.74). No statistical differences were found between BCS usage data and cancer stage, BMI, comorbidity, types of surgery, or cycles of chemotherapy. ConclusionsOverall, this study found considerable variability in the usage of app-based interventions. When health care professionals incorporate app-based interventions into their routine care for women with breast cancer, the learning and discussion functions of apps should be strengthened to promote engagement. Additionally, characteristics of women with breast cancer, such as age, level of education, income, and employment status, should be taken in consideration to develop tailored apps that address their particular needs and therefore improve their engagement with the app. Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12616000639426; http://www.ANZCTR.org.au/ACTRN12616000639426.aspx

Published
2020
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2. Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation

Author

Zhao, Yang, Guo, Peng, Gao, Han, and Chen, Xiuwan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Unsupervised domain adaptation (UDA) is an approach to minimizing domain gap. Generative methods are common approaches to minimizing the domain gap of aerial images which improves the performance of the downstream tasks, e.g., cross-domain semantic segmentation. For aerial images, the digital surface model (DSM) is usually available in both the source domain and the target domain. Depth information in DSM brings external information to generative models. However, little research utilizes it. In this paper, depth-assisted ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL), and depth cycle consistency loss (DCCL) are used to bring depth information into the generative model. Experimental results show that DRDG reaches state-of-the-art accuracy between generative methods in cross-domain semantic segmentation tasks.

Published
2022

3. ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation

Author

Zhao, Yang, Guo, Peng, Sun, Zihao, Chen, Xiuwan, and Gao, Han

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The performance of a semantic segmentation model for remote sensing (RS) images pretrained on an annotated dataset would greatly decrease when testing on another unannotated dataset because of the domain gap. Adversarial generative methods, e.g., DualGAN, are utilized for unpaired image-to-image translation to minimize the pixel-level domain gap, which is one of the common approaches for unsupervised domain adaptation (UDA). However, the existing image translation methods are facing two problems when performing RS images translation: 1) ignoring the scale discrepancy between two RS datasets which greatly affects the accuracy performance of scale-invariant objects, 2) ignoring the characteristic of real-to-real translation of RS images which brings an unstable factor for the training of the models. In this paper, ResiDualGAN is proposed for RS images translation, where an in-network resizer module is used for addressing the scale discrepancy of RS datasets, and a residual connection is used for strengthening the stability of real-to-real images translation and improving the performance in cross-domain semantic segmentation tasks. Combined with an output space adaptation method, the proposed method greatly improves the accuracy performance on common benchmarks, which demonstrates the superiority and reliability of ResiDuanGAN. At the end of the paper, a thorough discussion is also conducted to give a reasonable explanation for the improvement of ResiDualGAN. Our source code is available at https://github.com/miemieyanga/ResiDualGAN-DRDG.

Published
2022

16. Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface Clustering Algorithm

Author

National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Guo, Zhizhou, Liu, Baojian, Wan, Wei, Lu, Feng, Niu, Xinliang, Ji, Rui, Jing, Cheng, Li, Weiqiang, Chen, Xiuwan, Yang, Jun, Bai, Zhaoguang, National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Guo, Zhizhou, Liu, Baojian, Wan, Wei, Lu, Feng, Niu, Xinliang, Ji, Rui, Jing, Cheng, Li, Weiqiang, Chen, Xiuwan, Yang, Jun, and Bai, Zhaoguang

Abstract

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm3·cm-3. The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm3·cm-3. This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.

Published
2022

20. Recent Activities on Cal/Val of the First Chinese GNSS-R Mission Bufeng-1 A/B

Author

Jing, Cheng, Li, Weiqiang, Lu, Feng, Niu, Xinliang, Wan, Wei, Chen, Xiuwan, Di, Guodong, Rius, Antonio, Cardellach, Estel, Ribó, Serni, Liu, Baojian, and Nan, Yang

Abstract

Ponencia expuesta online en el 2021 Dragon Symposium celebrado el 22 de julio de 2021, Respect to the objectives and schedule of our project, the first-year report will include on-going activities and results of Bufeng-1 data processing, calibration workflow, and validation of the calibrated results on hurricane winds, soil moisture, and sea level measurements. The presentation has three parts. Firstly, a short introduction will be given about Bufeng-1 twin satellites that carry the Chinese first generation spaceborne GNSS-R instruments started using reflected GNSS signals to perform earth observation. Secondly, by utilizing the Bufeng-1 Normalized Bistatic Radar Cross Section (NBRCS), earth reflectivity, and range measurements, the preliminary results show that BuFeng-1 has a high agreement compared with other observations on severe sea surface winds, soil moisture, and sea level. In this presentation, the measurements of Bufeng-1 will be aligned with SFMR collected hurricanes, SMAP derived soil moisture, and DTU10 sea level models. Then, the validations of the accuracy and correlation coefficients will be analyzed to discuss the limitations and issues for the future research. For the last part, we will give the outlook about our future works of the objectives and the future plan of Bufeng missions.

Published
2021

30. Change Detection Method for High Resolution Remote Sensing Images Using Deep Learning

Author

ZHANG Xinlong, CHEN Xiuwan, LI Fei, and YANG Ting

Subjects
high resolution remote sensing, deep learning, lcsh:Mathematical geography. Cartography, change detection, lcsh:GA1-1776
Abstract

A novel change detection method is proposed based on deep learning to improve the accuracy of change detection in very high spatial resolution remote sensing images. On the base of image pre-processing, spectral and texture changes are extracted by modified change vector analysis and grey level co-occurrence matrix respectively, both concerning spatial-contextual information. Most likely changed and unchanged pixel-pairs are obtained by an adaptive threshold for selecting the labeled samples. The proposed model based on Gaussian-Bernoulli deep Boltzmann machines with a label layer is built to learn high-level features and is trained for determining the change areas. Experimental results on WorldView-3 and Pléiades-1 show that the proposed method out performs the compared methods in the accuracy of change detection.

Published
2017

35. Abstracts From the 13th World Congress of the World Federation of Critical Care Nurses and 10th Congress of the Serbian Society of Intensive Care, Anesthaesiology and Resuscitation (UINARS), Belgrade, Serbia, 25–28 October 2018

Author

Abeddin, Laila, primary, Ahmetbegovic, Marina, primary, Barkestad, Eva, primary, Cao, Shuyin, primary, Ceylan, Burcu, primary, Chen, Xiuwan, primary, Christensen, Mathilde Elsa, primary, Cingil, Dilek, primary, Coetzee, Isabel, primary, Cvijanović, Dušanka, primary, Dragica, Dobrović, primary, Dragosev, Gordana, primary, Du, Aiping, primary, Elmedin, Dubravac, primary, Dugonjić, Mario, primary, Friganovic, Adriano, primary, Fulbrook, Paul, primary, Goldsworthy, Sandra, primary, Ivanišić, Martina, primary, Ivankovic, Sanja, primary, Jankovic, Radmilo, primary, Jing, Wenwen, primary, Kleinpell, Ruth, primary, Küççük, Burcu, primary, Küçük, Sibel, primary, Kurtović, Sofija, primary, Lana, Rodić, primary, Lazarevic, Zorica, primary, Li, Qing, primary, Liao, Yao, primary, Liu, Huan, primary, Livesay, Sarah, primary, Lopez, Violeta, primary, Meštrović, Mirjana, primary, Mitchell, Marion, primary, Morales, Marcelo, primary, Mustu, Perver, primary, Nemes, Marjetka, primary, Panova, Gordana, primary, Peličić, Damir, primary, Ristic, Sasa, primary, Rodić, Lana, primary, Rogado, Maria Isabelita, primary, Saide, Faydalı, primary, José, Alicia San, primary, San José, Alicia, primary, Seferovic, Sasa, primary, Şerife, Kurşun, primary, Sheng, Han, primary, Shi, Li, primary, Simic, Marija, primary, Slijepčević, Jelena, primary, Stefanovic, Mirela, primary, Stojanovic, Emina, primary, Sü, Serpil Akbaş, primary, Sun, Jianhua, primary, Thorsteinsdóttir, Rósa, primary, Tian, Yongming, primary, MeryemTürkan, Işik, primary, Uçar, Ayşe, primary, Ugras, Gulay Altun, primary, Vidmanić, Sandro, primary, Vollman, Kathleen, primary, Vuckovic, Maja, primary, Wang, Limei, primary, Wang, Rong, primary, Wang, Chunyan, primary, Williams, Ged, primary, Wu, Yuchen, primary, Xu, Nana, primary, Xu, Yu, primary, Yao, Li, primary, Yeager, Susan, primary, Yu, Meng, primary, Zelic, Samela, primary, Zhang, Na, primary, Zhao, Lin, primary, Zhong, Jun, primary, Yang, Hui, additional, Li, Liming, additional, Zhu, Shichao, additional, Pei, Yongju, additional, Semiz, Mustafa, additional, Zhu, Haihua, additional, Klette, Maria Ingrid Bianca, additional, Hansen, Britt Sætre, additional, Gürel, Ayşenur, additional, Başer, Gülfidan, additional, Hisar, Filiz, additional, Milovanovic, Jelena, additional, Mirjana, Maletic, additional, Ristic, Sasa, additional, Stefanovic, Mirela, additional, Savic, Biljana, additional, Miljana, Gutovic, additional, Kurtovic, Sofija, additional, Maletic, Mirjana, additional, Dimitric, Daliborka, additional, Savić, Biljana, additional, Majstorović, Dunja, additional, Liu, Huan, additional, Xu, Yu, additional, Tian, Yongming, additional, Marcucci, Emanuela, additional, Williams, Ged, additional, Kleinpell, Ruth, additional, Alberto, Laura, additional, Witton, Nicola, additional, Šarika, Gajnok, additional, Belgin, Akin, additional, Tosun, Alime Selçuk, additional, Uludasdemir, Dilek, additional, Mustu, Perver, additional, Özeflanili, Ayşe, additional, Neuberg, Marijana, additional, Kišić, Kristina, additional, Hereković, Dijana, additional, Lukić, Anita, additional, Lukic, Branko, additional, Wu, Xiaoying, additional, Jin, Xi, additional, Guo, Zhi, additional, Du, Aiping, additional, Vrgoč, Jelena, additional, Bespaljko, Mario, additional, Lončar, Ivan, additional, Kucuk, Sibel, additional, Boshkovska, Jasna, additional, Panova, Blagica, additional, Dobrovic, Dragica, additional, Stamenkovic, Danica, additional, Stankovic, Miljana, additional, Antonijevic, Nikola, additional, Çakir, Büşra, additional, Çiftçi, Özlem, additional, Zhang, Hongyan, additional, Zhang, Limei, additional, Liu, Xueying, additional, Wang, Rong, additional, Li, Qingyin, additional, Yu, Meng, additional, Zhuang, Feifei, additional, Zhang, Lin, additional, Momcilovic, Marija, additional, Jovanovic, Jagica, additional, Jankovic, Suzana, additional, Demirören, Nesime, additional, Zhang, Qing, additional, Guo, Hailing, additional, Sun, Hong, additional, Wang, Xiaoting, additional, Yıldırım, Gülay, additional, Arslan, Selda, additional, Isik, Meryem Turkan, additional, Serinol, Hasan, additional, Babic, Sabina, additional, Friganovic, Adriano, additional, Vratan, Nikolina, additional, Jelic, Sanja, additional, Sheng, Han, additional, Yu, Huijie, additional, Wang, Limei, additional, Xia, Jianmei, additional, Zhang, Zhigang, additional, Zhang, Caiyun, additional, Tian, Jinhui, additional, Wang, Hao, additional, Zhou, Min, additional, Du, A., additional, Tian, Y., additional, Zhang, Zhi-Gang, additional, Tian, Jin-Hui, additional, Zhang, Cai-Yun, additional, Ding, Nannan, additional, Huang, D., additional, Yan, P., additional, Li, Q., additional, Li, Qing Yin, additional, Huo, ChunYing, additional, Hao, YunXIA, additional, Shi, Li, additional, Liu, Lin, additional, Zheng, Jili, additional, Xue, Yan, additional, and Zhang, Jingyi, additional

Published
2018
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36. Progress in Deep Learning Based Monocular Image Depth Estimation

Author

陈秀万 Chen Xiuwan, 王媛 Wang Yuan, 李阳 Li Yang, and 刘茂林 Liu Maolin

Subjects
Estimation, Monocular image, Computer science, business.industry, Deep learning, Computer vision, Artificial intelligence, Electrical and Electronic Engineering, business, Atomic and Molecular Physics, and Optics
Published
2019

45. Using remote sensing and GIS to analyse land cover change and its impacts on regional sustainable development

Author

Chen Xiuwan

Subjects
Sustainable development, Geographic information system, Land information system, business.industry, Remote sensing (archaeology), Thematic Mapper, Data analysis, General Earth and Planetary Sciences, Environmental science, Context (language use), Land cover, business, Remote sensing
Abstract

Policy and decision making in the context of sustainable development requires rapid, effective and efficient access to and integration of appropriate current information from a wide range of sources and disciplines, including land cover dynamic information derived from remotely sensed data. The analysis of data from high spatial resolution satellite sensors has potential in land cover monitoring. In this paper, a post-classification method is used to detect land cover change from multi-temporal satellite data, and particular attention is given to the selection of an appropriate method for land cover classification. The use of a Geographic Information System (GIS) allows further spatial analysis of the data derived from remotely sensed images and analysis of the impact of land cover change on regional sustainable development. The satellite-derived data used in this study are Thematic Mapper (TM) data acquired by Landsat-5 on 14 May 1985, 20 May 1987, 26 April 1990 and 20 May 1993 in Ansan City, Korea. The ...

Published
2002

46. Ground-based GNSS-R remote sensing experiment for retrieval of Soil Moisture Content in China

Author

Huang Dan, Hu Caiyi, Xiao Han, Yang Zhenyu, Cao Yanfeng, Li Wei, Chen Xiuwan, Zhen Han, and Li Huaiyu

Subjects
Data processing, Beijing, GNSS applications, Remote sensing (archaeology), Environmental science, Soil moisture content, China, Remote sensing
Abstract

This paper introduces the basic situation of Ground-based GNSS-R Remote Sensing Experiment for Retrieval of Soil Moisture Content in China, including the experiment principles, experiment site, experiment equipment, data processing and accuracy analysis. During past two years, Peking University carried out a series of ground-based GNSS-R experiments for retrieval of SMC in Henan Zhengzhou (Jan. 2012), Sichuan Ruoergai (Aug. 2012), Sichuan Shifang (Aug. 2013) and Beijing (Sept. 2013). The results show that the retrieval accuracy of this series of experiments is approximately 85%. Therefore, it could be stated that this experiment in China is very successful and the data is reliable.

Published
2014

47. A Method on Land Cover Classification by Combining Unsupervised Algorithm and Training Data

Author

Chen Xiuwan, Chung-Hyun Ahn, Ryutaro Tateishi, and Hu HePing

Subjects
Pixel, Contextual image classification, business.industry, Carry (arithmetic), Geography, Planning and Development, Pattern recognition, Land cover, computer.software_genre, Class (biology), Multispectral pattern recognition, Geography, Thematic Mapper, Data mining, Artificial intelligence, business, computer, Statistic, Water Science and Technology
Abstract

In this paper, a method on land cover identifying by combining unsupervised algorithm and training data (CUT) was developed. The procedures of land cover classification by using the CUT method are: (a) to carry out remotely sensed image classification by using an unsupervised algorithm (e.g. ISODATA unsupervised classification) to make a land cover classification map, MAP1, with n classes, where n is much greater than the proposed number of land cover classes, m, in the study area; (b) to collect training data for each of the proposed m classes; (c) to make a mask by using training data sets and statistically compute MAP1; (d) to assign the class h in MAP1 to class c in the final classification map, MAP2, if and only if the number of pixels in class c is with the maximum ratio at the statistic. The CUT method was also used to produce a land cover classification map in a test area, Ansan City of Korea, with Thematic Mapper (TM) data acquired by Landsat‐5. The accuracy analysis on the classificatio...

Published
1999

50. Wireless Local Area Network Assisted GPS in Seamless Positioning

Author

Chen Xiuwan, Ma Yan, and Xu Yubin

Subjects
business.industry, Computer science, Hybrid positioning system, Assisted GPS, ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS, Local positioning system, Global Positioning System, Satellite navigation, Wireless WAN, business, Precise Point Positioning, Computer network, Positioning technology
Abstract

Wireless Local Area Network (WLAN) is a new information access platform which can be used to achieve positioning, monitoring and tracking in wide area. Self-positioned of the network node is the basis and prerequisite for most applications, although the current popular WiFi positioning technology is a position solution for WLAN series standard (IEEE802.11). In this paper, based on the principle of satellite navigation and positioning with wireless sensor networks, the new method of combining GPS and WiFi networks is put forward to achieve seamless positioning and navigation. And the potential problems of existed technology are conducted in-depth discussion by summing up the seamlessly, current location and navigation technology, as well as analyzing existed problems and trends. Finally, the WiFi network assisted GPS positioning algorithm is analyzed in detail.

Published
2012

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