Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Selected Papers from the International Conference on Urban Intelligence and...
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Selected Papers from the International Conference on Urban Intelligence and Applications (ICUIA 2021)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (20 November 2021) | Viewed by 7670

Special Issue Editors

Dr. Xiaohui Yuan

E-Mail Website
Guest Editor
Computer Science and Engineering, University of North Texas, Denton, TX, USA
Interests: artificial intelligence; computer vision; remote sensing; robotics; health
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Baofu Fang

E-Mail Website
Guest Editor
Department of Computer Science & Technology, Hefei University of Technology, Hefei, China
Interests: Intelligence robot, Machine vision
Dr. Lichuan Gu

E-Mail Website
Guest Editor
School of Information & Computer, Anhui Agricultural University, Anhui, China
Interests: Sensor; Machine learning; Artificial intelligence; Internet of Things
Dr. Yuqi Fan

E-Mail Website
Guest Editor
Department of Computer Science & Technology, Hefei University of Technology, Hefei, China
Interests: artificial intelligence; resource allocation in networks; cloud computing; green network design; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The International Conference on Urban Intelligence and Applications (http://covis.cse.unt.edu/ICUIA21/index.html) will be held in Hefei, China on June 18–20, 2021. Authors of papers related to sensors presented at the conference are invited to submit extended versions of their work to this Special Issue for publication.

As a new generation of sensors and tools are being developed, municipal governments are formulating strategic plans to implement smart communities at different scales, and insights and solutions are urgently needed to innovate and manage urban spaces in order to improve livability and accessibility. The third International Conference on Urban Intelligence and Applications aims to bring together researchers, industry leaders, policy makers, and administrators to discuss emerging technologies and their applications to advance the design and implementation of intelligent utilization and management of city assets, thus contributing to the autonomous, reliable, and efficient operation of modern cities. Technical programs of this conference will encompass plenary talks, technical sessions, tutorials, and exhibitions.

Areas of interest include, but are not limited to, the following:

Technology and Infrastructure for Intelligence

  • Artificial intelligence and big data analytics for smart communities;
  • Deep learning and pattern recognition methods for smart communities;
  • Data visualization and simulation tools;
  • Advanced networking and intelligent sensors;
  • Internet of things (IoT) for smart communities;
  • Cloud, edge, and mobile computing and high-performance computing.

Community and Wellbeing
Smart home, building, and community;

  • Healthcare and wellbeing;
  • Smart grids and energy management;
  • Urban land management, sustainability, and livability.


Mobility and Transportation

  • Autonomous vehicle and management;
  • Transportation and traffic systems;
  • Robotics for community services;
  • Indoor/outdoor navigation and mapping.

Modern Agriculture and Agricultural Information

  • Agricultural Internet of Things;
  • Agricultural artificial intelligence;
  • Precision agriculture and agricultural robots;
  • Intelligence agricultural sensor detection;
  • Phenotype analysis of animals and plants;
  • Big data and agricultural information services.

Security and Emergency Management

  • Security, safety, and privacy of smart communities;
  • Disaster modeling and analysis;
  • Pollution monitoring and management;
  • Ethics of ubiquitous sensing and analysis technologies.

Dr. Xiaohui Yuan
Prof. Dr. Baofu Fang
Dr. Lichuan Gu
Dr. Yuqi Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

13 pages, 318 KiB  
Article
Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation
by Weizhong Ding, Qiubo Zhong, Yan Wang, Chao Guan and Baofu Fang
Sensors 2022, 22(3), 733; https://doi.org/10.3390/s22030733 - 19 Jan 2022
Cited by 9 | Viewed by 2225
Abstract
A new positioning algorithm based on RSS measurement is proposed. The algorithm adopts maximum likelihood estimation and semi-definite programming. The received signal strength model is transformed to a non-convex estimator for the positioning of the target using the maximum likelihood estimation. The non-convex [...] Read more.
A new positioning algorithm based on RSS measurement is proposed. The algorithm adopts maximum likelihood estimation and semi-definite programming. The received signal strength model is transformed to a non-convex estimator for the positioning of the target using the maximum likelihood estimation. The non-convex estimator is then transformed into a convex estimator by semi-definite programming, and the global minimum of the target location estimation is obtained. This algorithm aims at the L0 known problem and then extends its application to the case of L0 unknown. The simulations and experimental results show that the proposed algorithm has better accuracy than the existing positioning algorithms. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Urban Intelligence and Applications (ICUIA 2021))
►▼ Show Figures

Figure 1

15 pages, 513 KiB  
Article
A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism
by Cunli Mao, Haoyuan Liang, Zhengtao Yu, Yuxin Huang and Junjun Guo
Sensors 2021, 21(22), 7501; https://doi.org/10.3390/s21227501 - 11 Nov 2021
Cited by 3 | Viewed by 1929
Abstract
Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis. Existing text clustering methods usually based on topic models which only use topic and case infomation as the global features of documents, [...] Read more.
Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis. Existing text clustering methods usually based on topic models which only use topic and case infomation as the global features of documents, so distinguishing between different cases with similar types remains a challenge. The contents of documents contain rich local features. Taking into account the internal features of news, the information of cases and the contributions provided by different topics, we propose a clustering method of case-involved news, which combines topic network and multi-head attention mechanism. Using case information and topic information to construct a topic network, then extracting the global features by graph convolution network, thus realizing the combination of case information and topic information. At the same time, the local features are extracted by multi-head attention mechanism. Finally, the fusion of global features and local features is realized by variational auto-encoder, and the learned latent representations are used for clustering. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised clustering methods. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Urban Intelligence and Applications (ICUIA 2021))
►▼ Show Figures

Figure 1

17 pages, 1759 KiB  
Article
MOOC Behavior Analysis and Academic Performance Prediction Based on Entropy
by Xiaoliang Zhu, Yuanxin Ye, Liang Zhao and Chen Shen
Sensors 2021, 21(19), 6629; https://doi.org/10.3390/s21196629 - 5 Oct 2021
Cited by 6 | Viewed by 2508
Abstract
In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of [...] Read more.
In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance. Full article
(This article belongs to the Special Issue Selected Papers from the International Conference on Urban Intelligence and Applications (ICUIA 2021))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop