IECE - Institute of Emerging and Computer Engineers

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    Academic Editor
    Author
    Contributions by role
    Author 1
    Reviewer 21
    Editor 23
    Membership
    ORCID
    0000-0001-6234-1001
    Jinchao Chen
    School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
    Summary
    Dr. Jinchao Chen is an Associate Professor in School of Computer Science at Northwestern Polytechnical University, Xi’an, China. He has received his Ph.D. degree in Computer Science from the same institution in 2016. He focuses on the multi-processor scheduling, embedded and real-time systems, simulation and verification, decision-making and intelligent control of unmanned aerial vehicles, human-computer interaction systems. He has over 50 papers and 4 ESI highly-cited papers published in international conferences and journals (e.g., IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Industrial Electronics, IEEE Transactions on Cybernetics, IEEE Real-Time Systems Symposium). He is the Editor-in-Chief of ASP Transactions on Computers and ASP Transactions on Computers, and the Academic Editor of International Journal of Aerospace Engineering. He is a TCP member of many conferences and regular reviewer of IEEE Transactions on Industrial Informatics, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Transportation Electrification, IEEE Transactions on Vehicular Technology, and Concurrency and Computation Practice and Experience. He is a member of IEEE and CCF.
    Edited Journals
    IECE Transactions on Internet of Things
    IECE Transactions on Intelligent Unmanned Systems
    IECE Transactions on Computer Science
    IECE Contributions
    Articles
    1
    Edited
    23
    Free Access | Research Article | 07 April 2024
    Detection of Arctic Sea Ice Using 89 GHz Microwave Radiometer Channels
    by
    Yue Zhao
    ,
    Xingdong Wang
    ,
    Changfeng Luo
    IECE Transactions on Internet of Things | Volume 2, Issue 2: 36-43, 2024 | DOI:10.62762/TIOT.2024.528361
    Abstract
    Sea ice is a crucial component of the cryosphere, and extensive research has been conducted on sea ice using microwave remote sensing due to its robustness against cloud cover and illumination variations. This paper focuses on classifying Arctic sea ice based on microwave remote sensing data. Leveraging the high stability of microwave radiometers, we analyze the characteristics of different sea ice types across the Arctic region in January 2017 using high-resolution AMSR-E/AMSR2 data at the 89 GHz frequency band. Data at this frequency are less susceptible to cloud and water vapor interference, while lower frequency bands have traditionally been more commonly used in similar studies. However... More >
    Graphical Abstract
    Detection of Arctic Sea Ice Using 89 GHz Microwave Radiometer Channels
    Free Access | Research Article | 12 March 2024
    Advancements in Multi-Year Ice Concentration Estimation from SSM/I 91.6GHz Observations
    by
    Yue Zhao
    ,
    Xingdong Wang
    ,
    Zifan Zhang
    IECE Transactions on Internet of Things | Volume 2, Issue 1: 26-35, 2024 | DOI:10.62762/TIOT.2024.682080
    Abstract
    To enhance the LOMAX algorithm for sea ice concentration analysis in the polar regions, SSM/I 91.6GHz data was utilized, addressing the underuse of higher frequency data. The refinement process involved redefining PCT values for one-year and multi-year ice regions through both interpolation and least squares methods. Moreover, band operations were conducted to facilitate Arctic multi-year ice concentration retrieval. Comparative analyses with the NT algorithm indicated that the Arctic sea ice extents determined by both algorithms were similar, affirming the credibility of the modified LOMAX algorithm. When examining the results for March and September, the updated LOMAX algorithm demonstrate... More >
    Graphical Abstract
    Advancements in Multi-Year Ice Concentration Estimation from SSM/I 91.6GHz Observations
    Free Access | Research Article | 12 February 2024
    Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data
    by
    Zilin Wang
    ,
    Ping Zhang
    ,
    Weicheng Sun
    ,
    Dongxu Li
    IECE Transactions on Internet of Things | Volume 2, Issue 1: 20-25, 2024 | DOI:10.62762/TIOT.2024.186430
    Abstract
    The volume and complexity of data in various fields, particularly in biology, are increasing exponentially, posing a challenge to existing analytical methods, which often struggle with high-dimensional data such as single-cell Hi-C data. To address this issue, we employ unsupervised methods, specifically Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), to reduce data dimensions for visualization. Furthermore, we assess the information retention of the decomposed components using a Linear Discriminant Analysis (LDA) classifier model. Our findings indicate that these dimensionality reduction techniques effectively capture and present information not r... More >
    Graphical Abstract
    Application of Dimension Reduction Methods to High-Dimensional Single-Cell 3D Genomic Contact Data
    Free Access | Research Article | 14 January 2024
    3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms
    by
    Xue Li
    ,
    Jiali Qiu
    IECE Transactions on Internet of Things | Volume 2, Issue 1: 8-19, 2024 | DOI:10.62762/TIOT.2024.369369
    Abstract
    In light of the rapid advancements in big data and artificial intelligence technologies, the trend of uploading local files to cloud servers to mitigate local storage limitations is growing. However, the surge of duplicate files, especially images and videos, results in significant network bandwidth wastage and complicates server management. To tackle these issues, we have developed a multi-parameter video quality assessment model utilizing a 3D convolutional neural network within a video deduplication framework. Our method, inspired by the analytic hierarchy process, thoroughly evaluates the effects of packet loss rate, codec, frame rate, bit rate, and resolution on video quality. The model... More >
    Graphical Abstract
    3D Convolutional Neural Network-Based Multi-Parameter Video Quality Assessment Model on Cloud Platforms
    Free Access | Research Article | 11 January 2024
    Development and Evaluation of an IoT-Based Monitoring System for Patchouli Cultivation: An Integrated Approach to Enhance Agricultural Efficiency
    by
    Guodong Li
    ,
    Ping Zhang
    IECE Transactions on Internet of Things | Volume 2, Issue 1: 1-7, 2024 | DOI:10.62762/TIOT.2024.499965
    Abstract
    This study investigates the application of Internet of Things (IoT) technology in agriculture, focusing on the cultivation of patchouli, a medicinal plant known for its therapeutic properties. The research highlights the specific growth requirements and medicinal benefits of patchouli, and proposes a monitoring system based on ZigBee technology. The system's hardware design incorporates cc2530 and esp8266 chips for wireless data transmission, while communication between the OneNET cloud server and the MySQL database is managed through MQTT, TCP/IP, and HTTP protocols. This integration showcases the potential of IoT to significantly enhance agricultural efficiency by providing real-time data... More >
    Graphical Abstract
    Development and Evaluation of an IoT-Based Monitoring System for Patchouli Cultivation: An Integrated Approach to Enhance Agricultural Efficiency
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