Haojin Tang | Artificial Intelligence | Innovative Research Award

Dr. Haojin Tang | Artificial Intelligence | Innovative Research Award

Dr. Haojin Tang, Guangzhou University, China.

๐Ÿง‘โ€๐Ÿ”ฌ Dr. Haojin Tang is a Lecturer at Guangzhou University, specializing in ๐ŸŒ Artificial Intelligence, Deep Learning, and ๐Ÿ›ฐ๏ธ Hyperspectral Image Processing. He holds a Ph.D. in Information and Communication Engineering and has published 20+ top-tier papers, secured national patents ๐Ÿงพ, and led major research projects. As an inspiring mentor, he guides students to achieve excellence in intelligent manufacturing and environmental sensing. His work is shaping the future of smart technologies and remote sensing innovation. ๐Ÿš€๐Ÿ“ก

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๐ŸŽ“ Early Academic Pursuits

Dr. Haojin Tang’s academic excellence began at Shenzhen University, where he pursued a B.S. in Electronic Information Engineering (2014โ€“2018) and was recommended for postgraduate study without examination. He later earned both his M.S. (2018โ€“2020) and Ph.D. (2020โ€“2023) in Information and Communication Engineering, supported by the National Scholarship and recognized among the Top 10 Doctoral Dissertations. His solid academic foundation laid the groundwork for a promising research career in artificial intelligence and remote sensing. ๐ŸŽ“๐Ÿ“˜

๐Ÿ’ผ Professional Endeavors

Since July 2023, Dr. Tang has served as a Lecturer at the School of Electronic and Communication Engineering, Guangzhou University. He actively mentors undergraduate and graduate students, encouraging them to explore cutting-edge AI techniques in agricultural, forestry, and intelligent manufacturing applications. Under his supervision, students have secured high-impact publications and received numerous provincial and university-level gold awards. ๐Ÿ…๐Ÿ“š

๐Ÿ”ฌ Contributions and Research Focus On Artificial Intelligence

Dr. Tang’s research is rooted in the integration of Artificial Intelligence, Deep Learning, and Hyperspectral Image Processing, with special attention to industrial fault detection and few-shot learning. His contributions include:

  • Publishing over 20 papers in top-tier journals (JCR Q1, CAS TOP) and CCF Class A conferences.

  • Developing innovative algorithms for hyperspectral image classification and zero-shot learning.

  • Leading projects on cross-domain image classification using large language models. ๐Ÿง ๐Ÿ›ฐ๏ธ

๐ŸŒ Impact and Influence

Dr. Tang’s influence extends across academia and industry:

  • He has been invited to review for top journals including IEEE TGRS, Remote Sensing, and J-STARS.

  • His interdisciplinary research addresses real-world challenges in environmental monitoring and intelligent manufacturing.

  • His work has contributed to the advancement of UAV-based hyperspectral sensing and fault detection systems. ๐Ÿ“ก๐ŸŒฑ

๐Ÿง  Research Skills

Dr. Tang is adept at designing and implementing deep learning architectures for low-shot learning tasks, developing cross-domain classification algorithms, and leveraging large language models for image interpretation. His skills extend to UAV-based remote sensing systems, software development for big data analysis, and interdisciplinary innovation, making him a versatile researcher and practitioner. ๐Ÿค–๐Ÿ’ป

๐Ÿ… Awards and Honors

  • National Scholarship (Masterโ€™s & Ph.D.)

  • Top 10 Doctoral Dissertations at Shenzhen University

  • Student mentees under his guidance have won numerous provincial and institutional gold medals for research excellence.
    These accolades underscore his academic distinction and mentorship capabilities. ๐ŸŽ–๏ธ๐ŸŒŸ

๐Ÿ›๏ธ Legacy and Future Contributions

Dr. Tang is on a trajectory to become a leading innovator in AI-driven remote sensing and industrial diagnostics. His upcoming work on Large Language Model-driven image classification signals a bold move toward integrating generative AI into remote sensing. As a mentor and researcher, he is nurturing future scientists while paving the way for interpretable and scalable AI models in hyperspectral imaging and intelligent manufacturing. ๐Ÿš€๐ŸŒ

Publications Top Notes

  • ๐Ÿ›ฐ๏ธ A Spatialโ€“Spectral Prototypical Network for Hyperspectral Remote Sensing Image
    Journal: IEEE Geoscience and Remote Sensing Letters
    Citations: 64
    Year: 2019
    โœจ Pioneer in spatial-spectral modeling for remote sensing

  • ๐Ÿ” Multidimensional Local Binary Pattern for Hyperspectral Image Classification
    Journal: IEEE Transactions on Geoscience and Remote Sensing
    Citations: 37
    Year: 2021
    ๐Ÿ”ฌ Robust feature extraction in HSI

  • ๐Ÿง  Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification
    Journal: Remote Sensing
    Citations: 13
    Year: 2022
    ๐Ÿค– Hybrid deep learning for limited data

  • ๐Ÿ“Š A Multiscale Spatialโ€“Spectral Prototypical Network for Hyperspectral Image Few-Shot Classification
    Journal: IEEE Geoscience and Remote Sensing Letters
    Citations: 13
    Year: 2022
    ๐Ÿ” Improved generalization with few-shot learning

  • โš™๏ธ HFC-SST: Improved Spatial-Spectral Transformer for Hyperspectral Few-Shot Classification
    Journal: Journal of Applied Remote Sensing
    Citations: 12
    Year: 2023
    ๐Ÿงญ Enhanced transformer model in HSI

  • ๐Ÿ› ๏ธ Multi-Label Zero-Shot Learning for Industrial Fault Diagnosis
    Conference: 6th Intโ€™l Conf. on Information Communication and Signal Processing
    Citations: 7
    Year: 2023
    ๐Ÿญ AI for smart industry diagnostics

  • ๐Ÿ›ฐ๏ธ Multi-Scale Attention Adaptive Network for Object Detection in Remote Sensing Images
    Conference: 5th Intโ€™l Conf. on Information Communication and Signal Processing
    Citations: 4
    Year: 2022
    ๐ŸŽฏ Precision object detection framework

  • ๐Ÿง  Global-Local Attention-Aware Zero-Shot Learning for Industrial Fault Diagnosis
    Journal: IEEE Transactions on Instrumentation and Measurement
    Citations: 2
    Year: 2025
    ๐Ÿ’ก Breakthrough in industrial ZSL

  • ๐Ÿ“ TSSLBP: Tensor-Based Spatialโ€“Spectral Local Binary Pattern
    Journal: Journal of Applied Remote Sensing
    Citations: 2
    Year: 2020
    ๐Ÿงฎ Tensor-based HSI analysis

  • ๐Ÿงฌ AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification
    Journal: Remote Sensing
    Citations: 1
    Year: 2024
    ๐Ÿ”— Deep feature aggregation strategy

  • ๐ŸŽฏ Dense Convolution Siamese Network for Hyperspectral Image Target Detection
    Conference: 5th Intโ€™l Conf. on Information Communication and Signal Processing
    Citations: 1
    Year: 2022
    ๐Ÿ›ธ High-precision target detection

 

Shanggerile Jiang | Machine Learning | Best Researcher Award

Mr. Shanggerile Jiang |Machine Learning | Best Researcher Award

Mr. Shanggerile Jiang, University of Shanghai for Science and Technology, China.

Shanggerile Jiang ๐ŸŽ“ is a Research Assistant at the University of Shanghai for Science and Technology, specializing in Opto-electronic Information Science and Engineering. His work focuses on Affective Computing, Signal Processing, and Vocal Technique Assessment using Deep Learning ๐Ÿง . He has published in SCI-indexed journals ๐Ÿ“š and serves as a reviewer for reputed journals. A passionate IEEE student member โšก, he collaborates with leading professors to bridge technology and education through innovative AI applications ๐Ÿค–.

๐Ÿ‘จโ€๐ŸŽ“Profile

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๐ŸŽ“ Early Academic Pursuits

Shanggerile Jiang began his academic journey at the University of Shanghai for Science and Technology, earning a Bachelor’s degree from the School of Optical-Electrical and Computer Engineering in 2024. His foundational interest in engineering and technology set the stage for his focus on Opto-electronic Information Science and Engineering. His academic trajectory showcases a strong orientation toward computational and signal-based disciplines. ๐ŸŽ“๐Ÿ”ฌ

๐Ÿงช Professional Endeavors

Currently serving as a Research Assistant, Jiang is associated with the University of Shanghai for Science and Technology. His work centers on interdisciplinary research that combines optical communication, affective computing, and signal processing. He actively collaborates with esteemed professors and contributes to ongoing lab research and publications. ๐Ÿง‘โ€๐Ÿ”ฌ๐Ÿ‘จโ€๐Ÿ’ป

๐Ÿ”ฌ Contributions and Research Focus On Machine Learning

His primary research contributions include developing a Dense Dynamic Convolutional Network (DDNet) that surpasses traditional CNN and Transformer models in vocal technique assessment. His study explores EEG-based data augmentation using CWGAN and deep neural networks, reflecting his technical command over AI-based voice analysis and emotion recognition. ๐Ÿ—ฃ๏ธ๐Ÿ“Š๐Ÿง 

๐ŸŒ Impact and Influence

Jiangโ€™s work has made measurable progress in enhancing the accuracy and performance of Bel Canto vocal technique assessments, with potential applications in remote education and voice training. His top-1 accuracy of 90.11% and mAP of 41.89% establish his contribution as both reliable and practical. ๐ŸŽฏ๐Ÿ“ˆ

๐Ÿง  Research Skills

Jiang is proficient in Deep Learning, Machine Learning, and Artificial Neural Networks. He is also skilled in using computer-aided analytical tools for signal processing and affective computing tasks. His technical portfolio includes CWGAN implementation, dynamic CNN modeling, and EEG signal extraction. ๐Ÿค–๐Ÿงฎ

๐Ÿ… Awards and Honors

He has submitted his nomination for the Best Researcher Award. While major awards are in the future pipeline, his editorial reviewer roles for Education and Information Technologies and Biomedical Signal Processing and Control demonstrate early recognition and trust in his peer-review capabilities. ๐Ÿ…๐Ÿ“‘

๐Ÿ”ฎ Legacy and Future Contributions

Poised at the frontier of AI-based voice diagnostics and education, Jiang aims to further explore the intersection of neurotechnology and audio processing. His work holds long-term potential to redefine how affective computing can be used in educational and therapeutic environments. ๐ŸŒ๐Ÿš€

Publications Top Notes

๐Ÿ“˜ 1. Classic Vocal Performance Training Through C-VaC Method
Journal: Journal of Voice
Year: 2024
๐Ÿ“… Published on: October 14, 2024
๐ŸŽต Focus: Vocal performance, core muscle stability, computer-aided analysis

๐Ÿ“„ 2. Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano
Journal: ArXiv (Preprint)
Year: 2024
๐Ÿ“Š WOSUID: PPRN:118941218
๐Ÿ’ก Focus: Transfer learning, vocal data, mezzo-soprano classification